The Future of Work for the City of Seattle

Authors

This report was prepared by eight students at the Woodrow Wilson School: Peter Birke, Luciana Debenedetti, Gokul Gopalan Ramachandran, Michael Lachanski, Amy Williams Navarro, Alexander Pachman, Rocio Rodarte, and Brody Viney. The workshop was overseen by Professor Steven Strauss.

Disclaimer

The findings of this report represent the conclusions of the Authors and do not necessarily reflect the views of any individual author, Professor Steven Strauss, Princeton University, the City of Seattle, or any person or organization interviewed as part of this workshop (the Participants). The acceptance, rejection, modification, and/or implementation of any or all of the recommendations contained herein are accordingly subject to the discretion of the City of Seattle and/or other relevant organizations.

The content of this report is provided with the understanding that the Participants were involved with a student project, and the students were not engaged in rendering professional advice or services to the City of Seattle or to any other recipient of this report. This report is being made available “as is” without any warranty of any kind, either express or implied, including without limitation, implied warranties of fitness for any particular purpose. Further, the participants make no warranties, express or implied, as to the accuracy or adequacy of this report.

Acknowledgements

We thank the City of Seattle for the opportunity to engage on this timely policy issue, one with substantial long-term impact for its 12,000 employees. We specifically thank the Innovation & Performance Team, the Department of Human Resources, and the Office of the Deputy Mayor for their critical guidance and engagement throughout the study.

We also thank the stakeholders and subject matter experts within the City government, and throughout Seattle and New York City, for sharing their insights and expertise with the team.

Finally, we thank Professor Steven Strauss for the vital instruction, management, and perspective throughout the course. We also thank the staff of the Woodrow Wilson School Graduate Program Office for support throughout the project.

List of Abbreviations

AI: Artificial Intelligence

AMI: Advanced Metering Initiative

AV: Autonomous Vehicle

AQ: Automation Quotient

BLS: Bureau of Labor Statistics

CDO: Chief Data Officer

FTE: Full-Time Equivalent

HRIS: Human Resources Information System

GARE: Government Alliance on Race and Equity

IAC: Mayor’s Innovation Advisory Council

ITS: Intelligent Transportation System

ML: Machine Learning

MONUM: Boston Office of New Urban Mechanics

MVP: Minimum Viable Product

PIA: Privacy Impact Assessment

POC: People of Color

RSJI: Race and Social Justice Initiative

RSJEI: Race and Social Justice, Equity, and Inclusion

SDIT: Seattle Department of Information Technology

SDOT: Seattle Department of Transportation

SDHR: Seattle Department of Human Resources

SNAP: Supplemental Nutrition Assistance Program

STEM: Science, Technology, Engineering, and Mathematics

USPTO: United States Patent and Trademark Office

1: Executive Summary

Advances in digital technology, enabled by artificial intelligence, big data, robotics, and machine learning, are transforming the way that work is done in businesses and organizations of all kinds – including the public sector.

In this context, this report to the City of Seattle (the City) investigates how these trends will affect services and the municipal workforce in coming years.

We recommend the City embrace new technologies to improve service delivery, while also taking proactive steps to support RSJEI efforts and ensure its workforce is well placed to succeed in the workplace of the future.

New technologies provide an opportunity for the City to improve services for its residents.

In Section 2, we look at the global trends shaping technology and work. In Section 3, we explore how, by embracing new technology, the City can increase productivity, provide new ways to monitor and evaluate the effectiveness of policies, and improve the quality of service delivery.

The City is already experimenting with new technologies in areas as diverse as electricity and utilities distribution, transportation, and the arts.

However, we find there is significant scope for Seattle to adopt further innovations already used elsewhere. As new technologies emerge, the possibilities will only grow.

At the same time, technology will disrupt the tasks done by the City’s workforce.

New technologies facilitate the automation of tasks, not jobs, with routine or repetitive tasks easier to automate.

In Section 4, we find that around 31% of the typical tasks done by Seattle’s municipal workforce could be automated using current technology –although changes on this scale would take many years to implement.

The adoption of new technologies has the potential to relieve municipal workers of many difficult or repetitive tasks, freeing up time for work of greater value to the public. However, it will also challenge workers to develop new skills or even change roles altogether.

Figure 1.1: Estimated share of tasks done by City workers that can be automated with current technology

A continued focus on Race and Social Justice, Equity, and Inclusion (RSJEI) will be important for the City.

The City already has a strong focus on examining who is underrepresented in its workforce and ensuring recruitment and promotion processes are equitable and inclusive.

Because technological disruption can affect different workers in different ways, the City should continue its focus on these issues in coming years.

As Section 4 explores, at an aggregate level, the tasks done by women and men, and staff with different racial and ethnic backgrounds, all stand to be affected by automation approximately equally.

However, the pipeline of potential future employees in technology fields is still predominantly male, white and Asian. As the City looks to hire more workers with these skills, it will need to ensure it does not forego progress on RSJEI.

The City should adopt a clear framework for innovation and technology deployment.

The City has the chance to be a national leader by modeling best practices for the take up of new technology.

In Section 5, we recommend the City embrace a ‘laboratory’ approach to innovation, providing leaders with the freedom to experiment, to learn what works best, and even to fail as new approaches are tested.

At the same time, the City should build on existing processes to establish a framework for scaling technology

projects in a way that is equitable, including ensuring that effects on the existing workforce have been addressed.

The appointment of a Chief Data Officer to coordinate the use of data across agencies will support these efforts.

The City should invest in its existing workers to ensure they have the skills to succeed.

New technologies can only be deployed effectively if staff have the skills and knowledge needed to use them.

At the same time, it will be important to ensure existing workers are able to adapt to new roles and responsibilities as existing tasks are disrupted.

In Section 6, we recommend the City partner with Seattle City Colleges to create two new training programs for junior employees, focused on:

• ‘Soft’ teamwork, leadership and management skills; and

• Hard skills in IT, data analytics and business intelligence.

The City should also tap local private sector talent through a technology and data fellowship program.

The City should also take steps to ensure it can access a diverse talent pipeline.

As the City will increasingly compete with the private sector for skilled workers, in Section 7 we recommend partnering with Career Connect Washington to cultivate interest in public sector careers, starting in high school.

The City should also develop systems to track job applicants throughout the hiring process, and should test competency-based job advertisements, to protect against racial and gender biases in recruitment.

Appointing a Race and Social Justice, Equity, and Inclusion Officer and enhancing the role of RSJI Core Team 5 change agents in the hiring process would further support the City’s efforts on RSJEI.

Finally, the City should ensure its workforce management systems are fit for purpose.

Existing challenges with hiring and people management software, job classifications, and budgeted positions will only be exacerbated as technology changes the way the City works.

In Section 8, we recommend updating software to improve data analysis capacity and other functionality.

The City should also look to update its job classifications, simplify pay structures to improve upward mobility, and provide leaders with more flexibility over their staffing structure.

Together, these recommendations provide a roadmap for the City to prepare for the future of work.

Implementing these changes will present challenges of its own, and will need to be done in partnership with departments, staff, unions and residents themselves.

By taking steps now to begin to prepare for these changes, the City can model a best practice approach to innovation that is equitable, accountable, and serves the best interests of the City’s workforce as well as the Seattle community.

2: Digital Technology is Fundamentally Transforming the Nature of Work

“Our city of the future must also be one where people have access to opportunity and jobs.” – Mayor Durkan

Contemporary advances in digital technology, enabled by big data, artificial intelligence, and machine learning, are altering the way that work is done in organizations of all kinds, including in the public sector.

While adapting to technology has been a consistent challenge since the Industrial Revolution, these advancements represent unprecedented leaps in telecommunications, logistics and

supply chains, and financial services, to name a few. In turn, there are substantial opportunities for improved productivity and enhanced job fulfilment, just as there are challenges with such changes. As the City of Seattle prepares its municipal workforce for the future, it is critical to understand the national, and indeed global, trends driving changes in the labor market.

This section of the report examines such trends, provides a framework for their impact on workers, and identifies how these shifts will impact public sector work.

Global trends in technological change

The World Economic Forum notes that four particular technologies will be critical drivers of business growth and transformational economic shifts: ubiquitous high speed and mobile internet, artificial intelligence, widespread adoption of big data analytics, and cloud technology.1

Workforces, the Forum continues, will be affected along two parallel fronts: 1) large-scale decline in roles whose tasks become fully automated or redundant,

2) large-scale growth in new products and services, and in turn jobs, from the adoption of new technologies and other socio-economic developments.2

Digital technologies have broad applications.

Advances in technology have enabled the development of artificial intelligence and machine learning, which continue to reach new frontiers. This report focuses on the applications of such technologies for the provision of public services and the subsequent impacts on City employees that manage these services.

When discussing these digital technologies throughout the report, we are referring to the following:3,4

Artificial Intelligence (AI): the ability of computer programs/machines to

discern patterns and generate predictions from large data sets, to emulate and perform human tasks.

Machine Learning (ML): a subset of AI that trains a machine how to learn based on large data sets and examples to create algorithms that increase accuracy and performance, without specific coding from humans.

Automation: the creation and application of technology to monitor and control production and delivery of products and services.

Big Data: large volumes of data (structured and unstructured) that is so large, fast, or complex that it is difficult or impossible to process using traditional methods.

The applications of these technologies are substantial: predictive analytics, autonomous systems, and robotics, to name a few. Automation is of key concern because of the potential of many occupation-specific tasks performed by humans to be impacted.

Effects on productivity and employment are difficult to predict.

Predictions on the rate of technological adoption vary and scenarios abound on how such adoption will affect the labor market in the long-term. While the equivalent of millions of jobs are potentially automatable with today’s technology5, traditionally the automation of tasks has also resulted in the creation of additional jobs in the United States, this has proved consistent since the 1940s.

Further historical perspective is helpful to contextualize such trends. Labor

economists David Autor and Anna Salomons find that “technological progress has been broadly employment-augmenting and labordisplacing for at least three decades.”6 They find that shifts towards labordisplacing technologies in the 1980s affected the decline in labor’s share of value added, a dynamic that accelerated substantially in the 2000s. Nevertheless, they note that this subsequent acceleration cannot solely be attributed to technological changes.

Aggregate employment and productivity levels are important to consider, but there is also significant variation between industries. Subsequent research on specific technologies may help to shed light on more nuanced industry- or time-specific trends.

The impacts of technology on people and places will be uneven.

The benefits of enhanced productivity and job creation, perhaps unsurprisingly, do not accrue evenly. Many experts believe current trends in automation will increase gaps in the United States across existing cleavages: high-growth cities vs. struggling rural areas, and high-wage vs. low-wage workers, among others.7 Indeed, Carnegie Mellon economist Lee Bransetter cautions that policymakers should be less concerned about mass unemployment but rather about growing inequalities along a “pronounced skills bias.”8

Seattle, a high-growth city, has experienced impressive economic growth and population increases in recent years, but challenges remain on

how to promote increased economic opportunity for all residents. Though the State of Washington is on track to add 740,000 job openings in the next five years9, such challenges in access will likely continue. The City government will need to incorporate macro-level trends into its workforce development strategies, as its own employees will necessarily be impacted.

In short, continued automation will affect the structure of the labor force

over time, in uneven ways. While harnessing these technologies allows for positive economic growth, policymakers can promote inclusive growth in the face of long-term shifts by “connecting displaced workers with new opportunities, equipping people with the skills they need to succeed, revitalizing distressed areas, and supporting workers in transition.”10 The responses to technology-driven shifts, may be as consequential as the shifts themselves.

Effects of Technological Change on Workers

The Future of Work is both a continuation and an acceleration of trends in the economy. This section provides a framework for understanding how such trends affect workers.

Automation affects tasks.

Because jobs are comprised of many activities, a task-based framework allows researchers to analyze the susceptibility of particular tasks to automation, and to understand the resulting labor impacts.

Automation represents an unrivaled opportunity to change the nature of work in positive ways, particularly in terms of how workers spend their time and businesses allocate costs. Automation can improve the quality of the good or service being produced, reduce or eliminate mundane tasks, and reduce businesses’ operations costs. For example, General Electric implemented machine learningenabled automation by integrating supplier data to their contract

negotiation processes, generating $80 million in savings in its first year.11

Importantly, even though aspects of jobs can be automated, the nature of work will also change based on the tasks that remain. For example, in the public sector, the US Patent and Trademark Office has piloted AIassisted searches for initial patent reviews, and workers increased their productivity due to the enhanced inputs they received.12

The McKinsey Global Institute finds that “less than 5 percent of occupations can be automated in their entirety, but within 60 percent of jobs, at least 30 percent of activities could be automated by adopting currently demonstrated technologies.”13 Of course, these are averages and the impacts of automation differ across industries and occupations. Further in the report, we provide such estimates for the City government’s workforce.

Routine Tasks are at greatest risk.

Continued advances in such technologies will facilitate increasing automation of many routine, predictable physical and cognitive tasks. Because routine tasks follow precise, wellunderstood procedures, they are more easily codified by algorithms.

Routine tasks tend to be characteristic of many mid-wage occupations and, as researchers from MIT assert, “there has been an employment decline across clerical, administrative, support, production, and operations tasks.”14 Despite predictions of overall job growth and new occupations from technological adoption, the “hollowing out” of middle-wage jobs across the economy is expected to continue.15 In addition, demand is expected to increase for high skill and certain types of low skill jobs that are, in turn, more difficult to automate (e.g. health care workers, therapists and social workers, teachers, caretakers).

Nevertheless, there are still myriad aspects of workers’ tasks that cannot be distilled into predictable rules, because they rely on physical flexibility, common sense, judgment, intuition, and creativity. With present technologies, humans retain significant comparative advantage for many jobs.

Activities in many jobs will also be enhanced, and new responsibilities will emerge, rather than entire occupations becoming completely obsolete in the near term. For example, humanmachine pairing can create new responsibilities and workers can take advantage of opportunities to apply their skills and competencies to new or

related roles. In turn, workers will need to be trained to be more productive alongside software, platforms, etc., particularly to take advantage of the quality improvements that humanmachine pairings generate.

We understand, over time, that demand will decrease for occupations that have more automatable portions, while demand will increase for jobs with tasks less susceptible to automation.

STEM and Problem-Solving Skills will be in Demand.

Given the trends that favor high-skilled workers at the forefront of the knowledge economy, markets and employers will continue to reward science, technology, engineering, and math (STEM) based skills. Demand will also increase for social and emotional skills, as well as for higher skills linked to creativity and complex information processing.16

Roles with substantial expected growth include data analysts and scientists, software and applications developers, AI and ML specialists, big data specialists, process automation experts, and robotics engineers. On the other hand, roles such as customer service workers, marketing professionals, and organizational development specialists will also see increased demand.17 C-suite executives surveyed by the Boston Consulting Group and Harvard Business School note that recruiting workers with the requisite skills (e.g. hard skills, such as STEM-based skills, and “soft” social and emotional skills) for ever-changing jobs is their highest

priority for managing the future of work.18

Reskilling Workers is Imperative.

To be successful, workers will need to adapt to new demands and task composition. Governments and firms need to substantially invest in training and reskilling. By some estimates, “by 2022, no less than 54% of all employees [worldwide] will require significant re- and up-skilling. Of these, about 35% are expected to require additional training of up to 6 months, while 10% will require additional skills training of more than one year.”19 In addition, on-the-job training can equip workers with task-specific skills or complementarities, in scenarios where the nature of their work is complemented alongside AI-supported technologies.20

Failing to equip workers with the skills necessary for success in an everchanging economy will have significant repercussions: employers would be forfeiting additional productivity, and workers will be less likely to grow professionally and continue to contribute to the economy. The public and private sectors need to invest substantially in workforce training to increase employees’ ability to succeed in this competitive and changing landscape.

Applying a RSJEI Lens is Key to Mitigate Disparate Impacts.

Changes to the labor market will be particularly consequential for people of color and underrepresented groups in the United States, as analyses show that younger, less educated, and

underrepresented minorities work in more automatable occupations.21 In turn, many low-resourced communities do not have sufficient resources (for example, extra time, financial or family net worth) to finance continued education or independent training, making the role that government and industry can play in reskilling critical. Moreover, such groups are traditionally underrepresented in STEM occupations, which further limits future prospects.

According to the Brookings Institution, “average automation potential for U.S. occupations requiring less than a bachelor’s degree is 55 percent, more than double the 24 percent susceptibility among occupations that require a bachelor’s degree or more.”22 In addition, “African Americans could experience the disruptive forces of automation from a distinctly disadvantaged position, partially because they are often overrepresented in the ‘support roles’ that are most likely to be affected by automation (e.g. truck drivers, food service workers, office clerks).”23 Relatedly, the Hispanic/Latino population has a job displacement rate (percentage of jobs potentially lost due to automation by 2030) of 25.5 percent, slightly outpacing the African American rate of 23.1 percent.24

Firms and government should apply RSJEI lens to future of work strategies, to ensure that policies address inequities that can result along socioeconomic and demographic lines.

The public sector must harness automation to fulfill its mission over the long-term

While the private sector is at the forefront of technological adoption, it is imperative that the public sector keep up so it can provide the best quality service at the most reasonable cost.

Digital technology will transform how the public sector fulfills its mission.

IBM and the Partnership for Public Service find that, at the federal level, AI will impact government by transforming the federal workday, personalizing customer service, and increasing demand for technical and data skills.25

Deloitte finds that the Future of Work in Government will evolve in the following categories26:

• Work: public sector employees can create more value for constituents, and enhance their own workplace satisfaction. Scenarios with humanmachine pairing will become increasingly common.

• Workforce: government can take advantage of more varied work arrangements by accessing varied pools of skills and capabilities.

• Workplace: technology can change physical workspaces and arrangements (e.g. remote work), which can promote employee satisfaction and productivity.

Effects on public sector workers need to be considered.

Being intentional and critical with regards to technology adoption necessarily means employing a workercentric lens.

Engaging relevant stakeholders, particularly the actual workers, and their representatives (e.g. unions), is a critical first step for these analyses. Such considerations are especially key for entry-level and public-facing workers, whose jobs may have more tasks susceptible to automation.

Working with partners at the state level to strengthen regional strategies is another opportunity for the City. The Washington State Future of Work Taskforce’s 2019 Policy Report calls for comprehensive worker upskilling and lifelong learning, which the City can tap into for its own strategies.

Competition with the private sector for skilled workers will continue.

The challenge for governments is twopronged: competition with the private sector for human capital, and adequate workforce development. Accordingly, governments will need to increase investment in current and future employees by offering training and continuous education, fostering internal growth opportunities, and providing dynamic professional development.

By leveraging its key assets, such as being a mission-driven organization, and improving upon its existing systems, the City can better compete with other top employers in Seattle. In turn, by designing a workforce development strategy that incorporates digital transformation, the City can prepare its employees for the evolving demands they will face

3: Improving Public Services in Seattle with Technology

This section examines the opportunities and challenges in improving public sector service delivery by using digital technology.

As resident expectations of government continue to evolve, we find that are

many opportunities for the City to build on its existing use of technology, including by following the lead of peer cities and governments.

Residents expect and deserve high-quality services

The private sector is adept at using digital technology to provide an effective customer experience across a variety of goods and services. Residents expect, and deserve, to get a similar level of service provision from the public sector.

The public sector faces constraints and challenges in service delivery.

Traditionally, the public sector is budget-constrained and has to deliver services under limited resources. In turn, insufficient resourcing is a key reason for sub-optimal performance in the public sector, including delays, lowquality, and non-responsiveness.

Poor service provision, in turn, disproportionately affects underresourced socioeconomic groups.27 For example, many states in the US have implemented online platforms for the delivery of social services like Medicare and SNAP. But the user experience leaves much to be desired because of convoluted forms, use of old technology and lack of access via mobile devices.

Disadvantaged residents often face large opportunity costs in accessing such resources and benefits, for example due to financial or time constraints.

At the same time, the private sector consistently raises consumer expectations.

The private sector has been able to harness technological advances to open new frontiers in delivering goods and services. Technology has transformed a wide range of daily activities, from grocery shopping to consumer credit scoring.

This transformation of the consumer experience places pressure on public services, as residents come to expect similar levels of quality. Public opinion surveys illustrate this deficiency, with consistently lower satisfaction scores for public sector services in the public opinion surveys we analyzed.28,29 The result of such a recent McKinsey study on customer satisfaction for select services in the US is presented in Figure 3.1.30

Drilling down further, there is heterogeneity in customer experience across different types of government services.31 Such heterogeneity means that there are opportunities to target improvements and innovations towards specific services.

These challenges will grow for cities like Seattle.

Seattle is at the forefront of technological innovation. For example,

Amazon launched its first ‘Go’ stores in the city in 2018. There are no cashiers in these stores and consumers’ purchases are automatically charged to their Amazon accounts. This seamless experience has opened a new frontier in customer experience.

During our field visit, we learned that the Seattle Center is redeveloping its Arena. The new Arena will feature automated parking lanes and ticket counters to create an efficient parking experience.32 Once complete, residents will likely compare the Arena’s customer experience with other services provided by the City, which is likely to raise their expectations for the city’s service provision.

We believe that expectations from the public will only increase in the future, as private sector innovations continually improve user experiences. As customers experience continually improves with the private sector, it is reasonable to expect that there will be increasing pressure on the public sector to improve its services.

Digital technologies are an opportunity for Government to improve service delivery

The public sector needs to embrace digital technologies if it is to compete with customer satisfaction from the private sector in service delivery. These technologies improve service delivery by providing a higher quality product/service or a similar quality product at a lower cost.33 Both allow human (for example by reducing routine tasks) or financial resources

(through reduced costs) to be reallocated within the organization or to another sector. Such reallocations can also lead to gains that will spill over to other sectors.

Technologies can improve the speed and quality of information processing in public sector settings. This improvement can substantially reduce the time devoted to routine tasks,

Figure 3.1: US customer satisfaction across services.

Source: McKinsey (2017)

allowing employees to focus more on tasks requiring cognitive attention and interpersonal contact.

Technology will also improve monitoring and accountability. Governments struggle to enforce uniform standards due to limitations in resources.34 Fire inspection is a case in point, given the resource intensity

required to consistently monitor buildings and public spaces. City governments like New Orleans that have implemented AI-assisted fire inspection utilize predictive models based on historical data and defined characteristics to better assess risks and direct their preventive activities and inspections accordingly.35

The City can build on its existing use of digital technologies

The Seattle city government has made promising advances in the use of digital technologies for service provision. We also found heterogeneity in the adoption of technologies across city departments, which presents an opportunity for process improvement. This section reviews major recent technological interventions in the City.

Seattle City Light

The Advanced Metering Initiative (AMI) by City Light is a recent significant technological intervention in the City through the installation of Smart meters, a crucial component of Smart grids.

Smart grids are efficient electricity grids that use technological devices like smart meters, smart appliances and energy-efficient resources to reduce consumption and optimize peak load. The implementation of smart meters will lead to substantial cost savings, as has been shown in other jurisdictions such as the European Union.36 The implementation of this project is reviewed in detail in Box 3.1.

Department of Transportation

The Department of Transportation (SDOT) has deployed traffic cameras and sensors across the city under the Intelligent Transportation System (ITS).37 The Transportation Operations Center processes this data to provide real-time inputs to residents. Law enforcement officers and maintenance crews also use this information to plan activities and support decision-making.

The ITS has deployed bike detection sensors at traffic intersections, which communicate with traffic signals to optimize trips for cyclists.38 SDOT has also implemented automated traffic signaling, which responds to traffic disruptions. Automated signal timing has been particularly effective in clearing traffic after rail crossings or bridge openings.39

The current strategic plan of ITS expires in 2020, which represents an opportunity to continue to identify and implement additional technologies to improve its services.

Smart sensors under the Array of Things

A number of City departments have taken part in a project to deploy sensors across the city to measure rain, wind speed, air quality, and pedestrian traffic among other data.40 This data has the potential to support decision making on issues such as flooding, pollution, and traffic management.

The data is collected and processed by the University of Washington through a partnership with the City. The City

should evaluate the effectiveness of the project and consider expanding the project if it is judged to have been successful.

Many opportunities to improve services remain.

These are promising examples of technology adoption, and we believe Seattle can do more to thoughtfully identify, pilot, and scale digital technologies. With the right supporting framework, Seattle can further improve resident services with technology.

Learning from Seattle’s peers’ experiences with technology

Seattle can learn from the successful application of digital technologies by other governments. This section summarizes some such successful applications that Seattle can consider.

Unified 311 call center with chatbot support

An in-person visit, phone call, or an email are the usual first points of contact between residents and a public office. Assigning a dedicated person or team to attend to all inquiries can drain resources and is often too expensive for many public organizations.

Chatbots offer standardized responses to a wide variety of queries, in real-time at any time of the day, and are being increasingly adopted by governments. For example, the United Kingdom is experimenting with chatbots for nonemergency health and housing services and queries.41 Similarly, many American governments like the State of North Carolina and the City of Mesa, Arizona have rolled out chatbots.42

Chatbots are more effective if the city unifies resident queries under a single system, such as the 311 system in New York City. A unified call system for addressing residents’ grievances has many advantages. For example, the Mayor’s office can have a high-level view of what residents’ concerns are, and 311 can act as a platform to facilitate inter-departmental communication. A single system also allows for easier benchmarking of performance standards. The NYC 311 routinely evaluates its performance visà-vis other private service providers.

More than one hundred cities across the US have shifted to a unified 311 service.43 We note that a shift to a unified system requires significant interdepartmental coordination and is time consuming. As such, chatbots can be implemented even before a centralized system is in place, which offers an opportunity for a quick win.

Record Searching and DecisionMaking Support

Government employees spend a lot of time finding relevant precedents and assessing the legality of a process While many government offices already use some forms of advanced search and decision-making tools, AI is increasingly used to improve effectiveness in these fields and initial results are promising.

The US Patent and Trademark Office is currently working on developing an advanced search tool that uses machine learning algorithms.44 This innovation is an example of the humanmachine pairing we expect will increase in many workplaces. Through enhanced searches, officials can then save time from routine tasks and concentrate on more cognitively demanding ones, such as making a recommendation to accept an innovation for patent review.

Many Government offices are also experimenting with AI tools for generating reports and answers. For example, Japan is experimenting with

AI to draft policies and responses for Parliamentarians.45 These technologies can automate parts of routine work, thereby allowing employees to improve the quality of their output and overall productivity.

Identifying critical risks such as fire hazards, and road safety bottlenecks

Cities are reporting promising results after using predictive algorithms for identifying fire hazards.46 Fire departments traditionally organize inspections through random selection based on historical risk projections (and limited resources). However, such methods many times fail to identify the most critical buildings or areas at risk.

AI-enabled software analyzes reams of data through various algorithms to predict critical risks. The New Orleans and Atlanta fire departments have implemented such software, making fire inspections as much as two and a half times more efficient at identifying high-risk properties.47 Governments can use similar technology to predict forest fires, road safety bottlenecks, and other risks.

Managing the unique challenges of technology adoption

Digital technologies offer opportunities, but also create unique challenges to governance and accountability.

Racial Bias

Digital technologies raise issues of racial bias, equity and accountability. Research has documented racial bias embedded in policing and criminal justice algorithms in the US, resulting from reliance on historical data sets,

which have been shown to exhibit racial bias.48,49 A recent federal study found that false positives in facial recognition algorithms in the US for minority populations are orders of magnitude higher than that of Caucasians.50

The city should evaluate these risks as it considers any application of AI or ML to city services, and avoid high-risk technologies such as facial recognition.

Privacy

The efficiency of data-driven technologies is highly dependent on the quality of the data set inputs. However, the collection of this data can put at risk long-held notions of privacy and ethics. For example, in 2019 it was revealed that technology companies like Google, Apple, Facebook, and Microsoft had been collecting private conversations to improve their AI’s natural language capabilities, prompting a global regulatory and public backlash. 51,52

The Seattle city government is keenly aware of the importance of privacy, as one of the first city governments to establish a data protection and privacy framework.53 The city conducts Privacy Impact Assessments (PIA) of new technologies by assessing the privacy risks of projects and ways to mitigate them. We found that the city had performed a PIA on most of its technology projects, which is an important foundation for future technology adoption.54

Accountability

Government accountability requires that decision-making channels are transparent and open to public review. However, when governments procure services from the private sector to access the latest technologies, they are often limited in their ability to hold the technologies or companies to account. Such lack of transparency arises because companies develop proprietary algorithms, leading to the notion that many AI or ML goods and services are a ‘black-box’.55

Governments need to have the capacity to assess and monitor technological innovations created by the private sector, particularly because software and other products are procured with taxpayer funding. In some crucial areas, government may have to develop in-house solutions, and must subsequently develop in-house capabilities for all these services.

Accountability and transparency, as such, are significant challenges for the public sector as technologies become increasingly sophisticated.

Impact on employment

As digital technologies impact the workforce, governments will have to mitigate labor impacts. For example, Seattle City Light had organized paid retraining for meter readers when they implemented the Advanced Metering Initiative (see Box 3.1).

Similarly, the Paris subway worked closely with their drivers and unions for many years before one of their lines was automated. The management reached out to employees directly and through their unions. Management explained the needs and benefits of automation and worked out an acceptable re-deployment plan. This process took almost two years, which shows that such mitigation plans will be careful, painstaking work. Automation of the line started only three years later, giving both sides more time to come to a workable agreement.56

As these examples show, technology adoption in public services necessarily means incorporating the municipal workforce in any rollout.

Box 3.1: Advanced Metering Initiative (AMI) in Seattle City Light

The rollout of advanced meters in Seattle City Light is an excellent example of a successful implementation of digital technology. Seattle City Light started the installation of smart meters in new residential and small commercial construction projects in the summer of 2017.

The project had significant workforce implications, with forty-five Meter Reader jobs set to be automated and replaced. City Light committed not to lay off any workers and developed a redeployment plan by reaching out to workers to identify future job aspirations. They then worked with Seattle College to create a 12-week training program for the employees, paid for by City Light, which was conducted during regular work hours at full pay.

In total, thirty-three Meter Readers moved to new jobs, while the remaining Readers are still deployed for customers who opted out of the automated meters and for instances of meter failures.

City Light worked carefully to minimize challenges during the rollout by bringing all stakeholders on board early in the process. Customers were informed about the rollout through a dedicated web page and other communications channels.57 The Privacy Impact Assessment of the project was done well ahead of the rollout.58,59 They trained existing staff for using smart meters and retrained the affected employees.

We note that the rollout could have been this successful because only forty-five jobs were impacted, and recognize that a retraining and job placement effort for a larger group will be more resource intensive. Nevertheless, City Light’s rollout of advanced meters is a promising example of successful technology implementation and holds valuable lessons for future initiatives.

4: Analyzing the City’s Workforce and the Implications of Automation

This section provides a snapshot of the municipal workforce demographics, outlines the implications of automation for city employees, and provides an overview of the RSJEI challenges identified through our data analysis and the interviews conducted during our

visit to the city. This analysis motivates our additional RSJEI recommendations in subsequent sections of the report.

As the city prepares for the future of work, we believe it should continue to prioritize RSJEI efforts in its workforce.

Current Workforce Demographics and RSJEI Challenges

The findings below are based on Seattle’s internal administrative data provided to us in November 2019. The Seattle database includes data for the city’s 15,490 regular and temporary employees and 38 city departments as of October 2019.

The data includes information on each employee’s age, sex, self-reported race/ethnicity, department, current job title, length of time in current role, start date with the city, and salary. When relevant to contextualize, we compare the municipal workforce’s demographics to the city’s general population as captured in the latest American Community Survey (2018).60

Workforce Demographics

Our analysis of Seattle’s internal administrative data echoes many of the findings outlined in the Workforce Equity Update Report drafted by SDHR and the Seattle Office for Civil Rights in March 2019.61

Our data analysis reveals women are underrepresented in the municipal

workforce; however, we find no statistically significant evidence of a city-wide, department- and experienceadjusted gender wage gap in the City’s municipal workforce as a whole.62

Women are underrepresented in the municipal workforce

Only 40% of city employees are women in contrast to 49.8% of the Seattle population.63 This gap in representation is driven by the city’s five largest departments: the Police Department, City Lights, Parks, Seattle Public Utilities, and the Fire Department.

Additionally, we find evidence of gender sorting in 24 out of 28 departments analyzed (Figure 4.1). That is, women are overrepresented in traditionally lower paid departments and men are overrepresented in traditionally higher paid departments. We excluded ten departments that had less than 25 employees from our analysis so as not to skew the results.

Departments will differ from 50/50 breakdown by chance, as well as

Figure 4.1: Gender Sorting among Employees by Department

because of systematic factors. We define a department as sex-sorted if we find statistically significant64 evidence that the department’s deviation from a 50% male and 50% female ratio is not the result of chance.

While the municipal workforce is generally comparable to the city’s population, the numbers scratch the surface on equity

At a glance, the municipal workforce is relatively representative of the city’s workforce. Figure 4.2 compares the

city’s regular and temporary workforce to the city’s general population.

Latinx/Hispanic workers (6.5% of the city’s population; 5.6% of its regular workforce) are slightly underrepresented and Mixed-Race workers (6.8% of the city’s population and 3.9% of its regular worker force) are underrepresented in the city’s workforce.

Asian and Black/African American workers, while well-represented in the regular workforce, comprise a larger

part of the temporary workforce than they do of the permanent workforce, raising questions about the types of jobs available occupied by some employees of color.

While we elaborate on our qualitative findings later in this section, we must note that we do not believe these figures paint a complete picture of the barriers and challenges that people of color encounter in applying to or while working for the city.

While these numbers provide a general snapshot, to reach more comprehensive conclusions it is necessary for the city to collect more data on the application and employment pipeline (e.g. applicants’ progress through the interview process, employees’ promotional opportunities, employee performance metrics, educational attainment, completion of professional development programs).

A Qualitative Look at Race and Social Justice, Equity, and Inclusion in the City of Seattle

“We are facing unprecedented challenges as a city, and we want to have a myriad of voices at the table to ensure we are making decisions that will have the most impact and provide immediate relief within our communities.”65 –

This section builds upon our quantitative analysis with takeaways from our interviews with city employees and community stakeholders that shed light on RSJEI challenges that are not

captured in the data. The recommendations listed throughout this paper address the challenges outlined below.

Figure 4.2: Seattle Workforce Demographics

Mayor Durkan

Current RSJEI Initiatives in the City of Seattle

The City of Seattle has been a national leader in promoting race and social justice, equity, and inclusion in its workforce.66 Seattle’s Race and Social Justice Initiative (RSJI) was created in 2004 with the purpose of eliminating racial disparities and achieving racial equity in city government. In doing so, Seattle was the first city to launch a formal effort focused on ending institutionalized racism, a laudable initiative.67 The Government Alliance on Race and Equity (GARE) cites Seattle’s RSJI as a model racial equity infrastructure and highlights the its training and capacity-building strategy as a best practice.68

Mayor Durkan has prioritized equity to further advance these achievements. During her administration, SDHR and the Seattle Office for Civil Rights have released several reports on racial equity that provide a comprehensive overview of the city’s RSJEI strengths and challenges. Senior members of Mayor Durkan’s administration expressed that focusing on racial equity by developing strategies that work for communities that are being served least well by existing institutions and structures ensures that the city achieves better outcomes for all of Seattle’s communities. All agency heads and municipal employees we met with during our week in Seattle mentioned RSJEI as a priority.

Throughout this report, we use RSJEI to refer to Race and Social Justice, Equity, and Inclusion (RSJEI). This reflects the City of Seattle’s

commitment to leading with race when addressing equity and inclusion efforts RSJEI also implies addressing the systemic and structural barriers that have prevented certain racial groups from empowered participation and a sense of belonging.69

Interview Takeaways: Challenges in RSJEI

During our visit to Seattle in October 2019, we conducted interviews with employees from an array of municipal departments as well as community stakeholders. Members of our team also attended the RSJI Conference on October 29th, 2019, which allowed us to interview city employees that are particularly committed to promoting RSJEI in their departments. The challenges listed below were frequently mentioned during our conversations.

City job postings and job titles deter potential applicants

City job titles and postings are ridden with technical jargon, which we understand hinders the recruitment of employees from diverse backgrounds. “The city’s job postings cater to white people,” said a community leader, “The city needs to write job postings that are clear to the people they want to recruit.” This sentiment was echoed by several municipal employees who attended the RSJI Conference on October 29th, 2019. Further, the City of Seattle Employer of Choice Initiative June 2019 report identifies job titles as an area of opportunity to increase access to city jobs.70

RSJI Change Team members lack formal avenues to effect change and implement policies to foster RSJEI

RSJI Change Team members are groups of employees within each department that volunteer to facilitate the implementation of RSJI strategies and work plans.71

Change team members who attended the RSJI Conference expressed frustration due to the lack of resources and formal processes available to them to promote RSJEI

Many stated that they take on substantial responsibilities as change agents (e.g. attending workshops, facilitating dialogue in their departments), but do not have the authority to enforce policies or the opportunity to participate in decisionmaking around hiring.

Limited mobility and promotional opportunities for people of color

Employees observed that there are significant disparities in opportunity for people of color and women. Participants of the RSJI Conference and municipal employees described limited mobility and promotional opportunities for people of color.

We do not have information on employees’ career trajectory with the city to assess these observations further. The data available only provides information on an employee’s current position with the city so we cannot determine whether women or people of color are disproportionately looked over for promotions or career development opportunities. However, this was a common theme during conversations with employees.

Automation Implications for the City of Seattle’s Workforce

The increased use of technology in city service provision will be a key consideration as the city prepares for the future of work. Automation is a choice, though early adopters outside of the City could instigate the need for automation by changing standards for city services that over time affect the City’s own choices to automate. Many jobs in the economy today include tasks that can be done less expensively and often more effectively by a machine. Yet, across many firms and industries, these tasks are not outsourced to machines or software because of behavioral inertia, informational asymmetry (i.e. people do not realize

how much of their work can be automated), vested interests in maintaining the status quo, need for capital investment to pay for automation, and risk-aversion on the part of managers.

Historically, it has taken decades to exploit technologies that could eliminate redundant labor even for jobs which were 100% automatable. For instance, the technology for elevator users to direct their own elevators existed by the 1930s, but managers only began to push the technology after a series of strikes by elevator operators in 1945. Even so, the elevator occupation was a significant source of

employment into the 1960s and today still exists, albeit as a niche service.72

Methodology

First, we collapsed City of Seattle job categories from around 1200 to a more manageable 734. This primarily involved text string manipulation to combine similar sounding positions (e.g. Info Tech Prof C, B, and A were collapsed into a generic software engineering occupation).

Next, we selected the top 245 combined occupational categories, which represent around 83% of all employment for the city of Seattle. We used a calculator built by the Financial Times73 to count the number of tasks in a collapsed occupation that could be done by a machine circa 2017 when the calculator was introduced.

We consulted BLS occupational descriptions to link the Seattle job titlebased combined occupational categories to their Financial Times automation calculator equivalents. The ratio of automatable tasks to total tasks

is the automation quotient (AQ) of that combined occupational category.

Then, we linked our scores to the original dataset provided by Seattle’s HR department. We assigned each worker making above the minimum wage in one of our 245 combined occupational categories an AQ.

Apprentices were assumed to have the same AQ as their professions and included in the analysis. Student interns making above the minimum wage were excluded from the analysis. Seattle job openings that had no individual in them were also excluded.

Finally, using this linked dataset, we simply take the mean across different categories to calculate those categories’ AQ. We computed AQs for the city overall, AQs for selected occupations and departments, and AQs by race and sex. We found that, on average, there is little variation in AQ across demographic categories.

The AQ relies on two assumptions: (1) workers spend an equal amount of time

Box 4.1: Most and Least Automatable Jobs in the City

Most automatable job titles (approximately 753 jobs):

• Hydro Electric Operator

• Drainage Wastewater Collection Lead

• Solid Waste Field Representative Lead

Least automatable job titles (approximately 519 jobs):

• Counselor

• Community Development Specialist

• Victim Advocate

• Human Services Supervisor

• Social Services Aide

on all tasks and (2) tasks are comparable between the public and private sectors.

We understand that the first assumption is, at best, a loose approximation to reality, but the conceptual framework underlying the FT calculator constitutes the dominant lens through which economists forecast future automation and analyze past automation

Automation will have significant effects on the city’s municipal workforce

We find that automation will create substantial employment change for the city’s workforce. This disruption will have roughly the same impact across gender and race.

However, if we look at the most automatable jobs (Box 4.1), we find that men will be disproportionately impacted, which is consistent with national-level projections.

31% of the current task load could be automated

While 31% could be perceived as high on an absolute scale, the US and global average is 50%. The automation of these tasks would amount to approximately 3,000 full-time equivalent employees.

Nevertheless, it is important to note that no single job title can be completely automated. For example, a strategic adviser is 31% automatable on average, while a police communications dispatcher and a city attorney’s assistant are 32% and 30% automatable, respectively.

Jobs occupied by men and women have similarly automatable task loads

31.7% of the tasks performed by male employees and 30.8% of the tasks performed by female employees can be performed by a machine. These findings are in line with national and global figures that estimate that women are slightly less at risk than men of being affected by automation.74

However, men occupy the job categories that are most likely to be automated

68% of the current job holders for the most automatable jobs are men. A job is considered “very automatable” if more than 60% of the tasks can be done by machines.

In contrast, women hold 69% of the least automatable jobs. We consider a job “not very automatable” if less than 15% of the job’s task can be performed by machines.

Automation has the same effect across race and ethnicity

Employees of color will be affected by automation at a similar scale than the rest of the workforce. The automatable task loads for each group are:

• 31.9% for African Americans

• 31.4% for Whites

• 30.3% for Asians

• 30.2% for Hispanics

Nonetheless, lack of diversity in the Science, Technology, Engineering, and Math (STEM) pipeline poses a challenge for the city as it prepares for the Future of Work

Even though automation does not pose a disproportionately large threat to women or POC in the city’s workforce, women, and POC (with the exception of Asian and multiracial individuals) tend to be underrepresented in the pipeline for STEM careers (Figures 4.3 and 4.4).75

Assuming the demographic break down of students in Seattle who pursue STEM degrees roughly parallels national trends, this could have serious implications for RSJEI in the city’s future workforce since it is our expectation that STEM workers will need to be an increasing percentage of the City’s workforce.76

The city must maintain RSJEI at the top of its priorities as it deepens its strategy around upskilling and enhancing its workforce pipeline.

In other words, there is a risk that the city will renege on progress with RSJEI goals if it does not seek to address the pipeline issue vis-à-vis the STEM skills that will be in demand.

Figure 4.3: Post-Secondary Degrees in STEM by Gender in Academic Year 2016-201777

Figure 4.4: Post-Secondary Degrees in STEM by Race/Ethnicity in Academic Year 2016-201778

5: Establishing a Framework for Innovation & Technology Deployment

Seattle should embrace the deployment of digital technologies while mitigating their risks. To achieve this, Seattle should develop a clear framework for innovation and technology deployment among its agencies.

In doing so, the City has an opportunity to serve as a model for other cities on how to deploy digital technologies in transparent and equitable ways.

This section recommends that the City of Seattle establish itself as a laboratory

for digital technology deployment and develop a framework for scaling successful pilots.

A laboratory approach can enable city agencies to experiment with new technologies and service delivery models in low-risk environments. Meanwhile, establishing a framework for scaling successful experiments, and an ecosystem of related institutional actors, will ensure that the city is embedding equity within its major technology initiatives.

Approaches to innovation in Seattle

The U.S. Department of Commerce defines innovation as “the design, invention, development, and/or implementation of new or altered products, services, processes, systems, organizational structures or business models for the purposes of creating new value for customers.”79

Innovation enables city agencies to find new ways to deliver services and increase the quality of life for residents.

However, encouraging innovation can be challenging for governments. Since city governments provide vital functions to their residents, such as supplying safe drinking water and ensuring public safety, there is often a view that experimentation – and its associated risk of failure– is simply not an option.

Seattle, like most municipal governments, currently possesses an ad hoc approach to technology experimentation and service delivery innovation.

While there are emerging pockets of innovation such as the Innovation & Performance team and the recently launched Innovation Advisory Council (IAC), most city agencies we spoke to were not encouraged to experiment and assumed a risk averse position.

This ad hoc, risk averse approach presents a missed opportunity for the City on many fronts. First, the approach hinders City agencies’ ability to identify new performance improvements that could improve quality of services residents receive.

As a result, agencies are slow to change or iterate on business operations and become reliant on antiquated, legacy systems.

And when agencies do decide to adopt a new technology, the lack of systematic field testing prior to large scale implementation can lead to failed

technology deployments where course corrections are costly and timely. Instead, the City should encourage experimentation across its agencies to improve the quality of its services while building the risk tolerance and evidence base needed to effectively scale new technologies

Recommendation 5.1: Embrace the “City as Laboratory” approach to technology deployment

Embracing innovation requires establishing the right culture, framework, and guardrails for experimentation across city agencies. It is often employees who are closest to customers who are in the best position to innovate, which underscores the need to build a culture of innovation across all levels of city government. Additionally, leveraging user-centered design principles and community engagement tools will ensure the laboratory approach will be centered around RSJEI efforts as well as innovation.

With this culture as a foundation, a laboratory approach to service delivery innovation and technology deployment has become more prominent across major U.S. cities. For example, Box 5.1 presents a case study from Boston’s Office of New Urban Mechanics. A laboratory approach to innovation is distinguished by six key activities.80

• Identify Appropriate Risks: While a laboratory approach embraces failure, it is essential to identify which failures are acceptable and

which are not. Managers should mitigate harmful risks and avoid experiments that have inequitable impacts on vulnerable populations or jeopardize the public’s health, safety, and privacy.

• Generate a Vision: Under a laboratory approach, managers must have a vision of the problem they are trying to address as well as a potential solution for that problem. A good vision requires proximity to the end user, as well as soliciting and listening to user feedback through community engagement to understand the problem. Familiarity with design thinking principles such as journey-mapping and ideation can help formulate solutions.

• Form a Hypothesis: Managers transform their vision into a testable hypothesis. They flesh out the key conceptual and operational elements in a way that can be tested and proven right or wrong.

• Build Minimum Viable Products: A hypothesis is built into a minimum

viable product (MVP), the simplest version of a product or service to meet the needs of customers while providing feedback for refinement. MVPs enable managers to maximize real-world feedback while minimizing the time spent developing untested products.

• Test with Users: Managers should prioritize bringing their MVPs to users for testing. Doing so allows them to quickly build an evidence base on the product’s performance.

• Learn and Refine: Finally, managers should learn from user testing and evaluate the evidence to determine whether the experiment validated the initial hypotheses or not. Successful projects should be candidates for scaling while unsuccessful projects should be refined or discontinued However, these unsuccessful experiments should not be treated as failures. All experiments provide valuable insights that inform and aid agency decision-making.

Box 5.1: Boston Office of New Urban Mechanics & Autonomous Vehicles

Boston’s Office of New Urban Mechanics (MONUM) is the city’s hub for experimentation and civic innovation. Established in 2010, MONUM launches experiments to test new ways to improve the quality of life for Bostonians across the topic areas ranging from public health to transportation to housing. If experiments are successful, MONUM works with the relevant city agencies to scale the experiment into a permanent service.

In 2016, MONUM launched an experiment to test autonomous vehicles (AV) in Boston. AVs hold the promise of reducing traffic deaths, lowering vehicle emissions, and increasing access to public transportation. However, there is uncertainty about what regulations are needed to ensure AVs are deployed in a safe and responsible manner. MONUM’s experiment sought to provide policymakers more clarity to inform future transportation policies.

MONUM restricted the experiment to ensure the safety of residents. AV companies were required to meet safety standards and protocols before gaining permission to test their vehicles. In addition, MONUM limited AV testing to a finite area within the city’s Seaport District. To date, the experiment has facilitated over 3,000 miles of autonomous driving and based on its performance thus far, the City has authorized one AVs company to begin testing its vehicles citywide.81

MONUM’s AV experiment illustrates the benefits of a laboratory approach to developing and launching new programs. By testing their hypothesis that AVs can improve transportation for the city, the experiment equipped city policymakers with real-world data on how AVs perform in urban environments, enabling them to craft regulations to meet the challenges and opportunities that AVs present, while also improving safety by allowing companies to refine their AVs in real-work conditions 82

A laboratory approach also requires a degree of flexible funding. We recommend creating innovation funds for major city agencies to more rapidly pilot small proof-of-concept initiatives.

The Seattle Innovation & Performance team already embodies many of the key laboratory activities, and is a natural champion for this approach.

Scaling What Works in Equitable Ways

The “City as Laboratory” approach can unearth new ways to deliver city services and improve the quality of life for Seattle residents. However, it is important for the City to ensure new technology deployments are scaled in a responsible manner. To that end, the City should have a framework in place to evaluate any deployment’s effect on key issues such as safety, privacy, RSJEI, and impact on the municipal workforce.

It is also important for the city to have institutional actors in place to implement these changes. In our

conversations, we learned about the IAC’s internal review board that evaluates their projects across privacy, ethics, and other standards. The board could be adapted to ensure that city leaders can evaluate major citywide technology projects and ensure important safeguards are met prior to scaling.

Meanwhile, a Chief Data Officer can serve as a catalyst for embedding data analytics into agency operations to aid in decision making and encourage continued accountability around technology.

Recommendation 5.2: Broaden the Innovation Advisory Council’s Internal Review Board purview to all major city technology projects

The IAC’s internal review board ensures projects meet standards related to privacy, ethics, inclusion, procurement, and open data – among others. The board includes the City’s Chief Privacy Officer, the City’s Director of Ethics, and representatives from the City Attorney’s Office and Seattle IT.

The board was identified by city stakeholders as a good foundation from which to adapt to major citywide technology deployments. Given the potential impacts of digital technologies

on the municipal workforce, this board should also examine the workforce impacts that any citywide deployment would have.

To that end, the board should also include representation from the Seattle Department of Human Resources, the City’s Labor Relations team, and the Race and Social Justice, Equity, and Inclusion Officer – should the City establish the position (as we discuss in more detail in Section 7).

Recommendation 5.3: Appoint a Chief Data Officer

A Chief Data Officer (CDO) helps build the capacity for data-driven decisionmaking across city agencies. They can serve the important role of champion and catalyst in the deployment of data in both small-scale experiments and major technological transformations. In doing so, they are a key component to a city as laboratory approach.

Many of Seattle’s peers have already established a CDO position. Sixteen major U.S. cities have a CDO including New York, Los Angeles, and Philadelphia.83

Seattle should establish a Chief Data Officer position to accelerate the use of data analytics in agency operations.

We recommend providing the CDO with a small team to operationalize data-

Implementation timeline

Within 12 months:

• Develop guidelines for laboratory approach & identify pipeline of potential experiments (5.1)

• Agree on adapted IAC rubric with guidelines around municipal workforce impacts (5.2)

• Add SDHR representative to the IAC Internal Review Board (5.2)

driven initiatives. For cities with a CDO, the size of the CDO’s team varies, ranging from one to thirty. We would recommend starting with a team of 2-3 analysts who can carry out tasks associated with business process analysis, data analysis & visualization, and project management.84

Reporting structures also vary by city. Some cities, such as Los Angeles, have their CDO report to the mayor or deputy mayor. Others, such as San Diego, have embedded their CDO within their Performance & Analytics team.85 Given their current role in spurring the use of data analytics across city agencies, the Innovation & Performance team could be a natural fit to house the role in Seattle.

• Adapted IAC rubric in place for all major technology projects (5.2)

• Innovation Advisory Board in place to review all major technology projects (5.2)

• Establish Chief Data Officer housed in the Innovation & Performance team (5.3)

• Chief Data Officer possesses team of three additional analysts within Innovation & Performance team (5.3)

Within 3 years (2022):

• Agencies engaged in experiments, with support from Innovation & Performance team (5.1)

6: Workforce Development Strategies for Seattle’s Current Employees

A central conclusion outlined in prior sections of this report is that technology adoption will change the way the City of Seattle delivers services, with important implications for municipal workers. This section explores how Seattle can meet this challenge and ensure incumbent employees are prepared for changing workplace demands. In particular, the City should:

• Make better use of existing policies to promote workforce development.

• Create new training programs for existing employees.

• Tap into Seattle’s private sector technology talent to help develop municipal employee skills.

We consider a broad definition of workforce development, which includes any policies or programs that provide employees with opportunities for a continued and lasting livelihood, while helping the City achieve its service delivery goals.86 Our conception of workforce development builds on workforce equity best practices, implying a workforce that is inclusive of people of color and other marginalized communities at rates that are representative of Seattle at all levels of City employment.87

The Future of Work will Increase the Importance of Employee Trainings

As established in Section 2, continued advances in digital technology are creating demand for new skills among workforces globally, which increases the need for worker trainings.88

For example, academics cite creativity and innovation as two skills with growing importance to worker productivity.89 This trend may accelerate as routine cognitive and manual tasks become increasingly automatable.

Moreover, the skills needed by workers are constantly evolving, making it difficult for individuals to enter the

workforce with all the tools they will need to succeed in the future. These dynamics increase the importance of on-the-job trainings.

Workforce Development Programs Can Improve Workplace RSJEI

A human-centered approach to workforce development can strengthen workforce RSJEI efforts by expanding access to development opportunities.90

Building entry-level employees’ skills and talents is particularly important for the City as senior leaders consistently stated that women and people of color

disproportionately hold entry-level positions and lack sufficient opportunities for advancement.

In line with the City’s prior work on upskilling, we believe providing entrylevel employees with the resources they need to advance in City government can help Seattle become a leader in building a diverse, equitable, and inclusive workplace.91

Current Workforce Development Offerings Leave Gaps

The City offers a wide range of training programs to its employees.92 However, access to these programs is often limited to specific departments. For example, entry-level employees in just two departments have access to a tuition reimbursement program.93

The result is what some senior leaders termed as a “scattered” approach to workforce development, where employees from some departments are afforded workforce development opportunities and others miss out.

According to senior leaders, entry-level employees currently lack the tools they need to advance their careers. While department-specific trainings may not be relevant to all City employees, there is a clear need to equip workers with the

skills to thrive as technology changes the way the City delivers services.

“Out-of-Class” Assignments Represent an Untapped Opportunity

Current City personnel policies allow managers to temporarily assign employees the duties and pay levels of higher, “out-of-class”, positions. Assignments are demand-driven and used in scenarios such as parental leave, position vacancies, peak work periods, or special projects.94

Based on our field interviews, many senior leaders do not see these openings as a workforce development tool, despite language in the policy that licenses the use of out-of-class assignments for career development. The perception seems to be primarily a question of culture rather than policy.

Seattle Should Invest in Developing Employees’ Skills

To meet the needs of the future, we recommend the City invest in additional workforce development programs to equip employees with both the soft and hard skills they need to grow into future leaders within the City.

These investments need not be limited to new training programs but can also leverage the City’s existing policies and Seattle’s private sector talent.

Recommendation 6.1: Effectively Leverage “Out-of-Class” Assignments as a Workforce Development Tool

We recommend Seattle encourage the use of out-of-class assignments as a workforce development tool. To begin transforming the current culture

surrounding out-of-class assignments, senior leaders need to send a clear and committed message. Leadership buy-

in is a requisite step to shifting organizational culture.95

To begin this culture shift, the Mayor’s Office can develop a policy revision, based on input from counsel and solicitation from the City’s unions, exhorting the use of out-of-class assignments as a workforce development tool. Doing so will first require garnering buy-in from department directors and labor representatives.

Subsequently, the Department of Human Resources should conduct a series of meetings with human resource teams and key hiring managers across departments to further foster buy-in around the benefits of developing current employees’ skills through out-of-class rotations.

We believe it is important that hiring managers understand that a new approach to out-of-class assignments is a priority of department directors and the Mayor’s Office.

Recommendation 6.2: Create Two New Workforce Development Trainings for City Employees to Develop Soft and Hard Skills

The City should invest in two new workforce development programs, made available to employees across departments.

First, we recommend the City offer a credentialed program aimed at providing entry-level employees with the leadership skills they need to grow into senior roles. Next, we recommend a second credentialed program to provide current employees with the technology and data analytics skills they will need to thrive in the future.

We consider Seattle Colleges to be a natural partner to offer these development programs given the number of customized workforce development solutions organized by the Colleges for employers throughout the Seattle area.96 For both programs, the City should proactively engage employees from underrepresented groups, such as women and people of color, to ensure robust participation.

We believe these engagement responsibilities can fall under the portfolio of a new Race and Social Justice, Equity, and Inclusion Officer, which will be described in more detail later in this report.

Additionally, it is important to develop buy-in among hiring managers as the training credentials must be viewed seriously within City government if they are to serve as productive tools in employees’ career progressions, which is critical to employee take-up.

Create a New Soft Skills Leadership Development Program

The City’s new workforce development program, focused on developing entrylevel employees’ soft skills such as leadership, problem solving, and creativity, will help prepare City employees to meet the needs of the future and address concerns raised by senior leaders regarding the insufficient assistance offered to prepare entry-

level employees to take on more senior roles in City Government. The program should incorporate workforce development best practices such as:

• Convening a working group of hiring managers across departments to determine which skills to develop.

• Piloting the program with one department before scaling the offering City-wide.

• Ensuring the credential is valued by hiring managers and a genuine tool in advancing entry-level employee’s careers in the City.

• In line with personnel training rules, treating leadership training as a paid work responsibility.

• Offering trainings during work hours and holding sessions on-site.

Offer a New Technology and Data Analytics Upskilling Program

According to leading Future of Work research, Seattle’s employees must receive targeted and continuous technical upskilling to respond to future demands for new skills and job tasks, with an emphasis on human-machine pairing.97

As demand for technology and data capabilities grows, Seattle’s municipal employees will need new technical skills to participate in the City’s jobs of the future. The new technology and data analytics workforce development program should also incorporate the workforce development best practices outlined above.

Recommendation 6.3: Launch a Civic Technology Fellowship Program for Local Private Sector Technologists

Building off Mayor Durkan’s existing efforts to tap into Seattle’s private sector talent through the Innovation Advisory Council, we recommend the City launch a new government Civic Technology Fellowship program for local employees in the technology sector. The program would offer private sector employees an opportunity to give back to the community through a mutually beneficial exchange.

We envision the program as a 12month fellowship, where private sector employees working in the technology and data analytics fields are embedded within a City department to complete a project alongside City employees.

It will be critical to foster buy-in among participating City departments and employees as it is important the program not be received as a mechanism for private sector employees to instruct City workers on how to do their jobs. It will also be important to develop buy-in among relevant unions and can be thought of as one piece of a holistic workforce development upgrade.

The fellowship program could help develop the City’s technology and data analytics capabilities through a complementary approach to traditional workforce development offerings.

Box 6.1: Civic Technology Fellowship Model: Code for America

The Code for America Community Fellowship program places highly capable technology professionals in city governments across the country to tackle salient community issues focused on increasing equity and inclusion of government services for vulnerable populations. Fellows undergo a highly competitive application process that selects just 5-10% of applicants from a pool of experienced, mid-career professionals working in computer engineering, design, research, data analytics, and project management.

Participating technologists spend 6 months collaborating with their home-city municipal government to provide user-centered, iterative, data-driven solutions to urgent community issues. For example, 2019 Fellows in Miami and Santa Monica are working on technology projects to make affordable housing more accessible. The Community Fellowship program placed 17 fellows in 7 Cities in 2019.98

Implementation timeline

Within 12 months:

• Mayor’s Office to foster buy-in for use of out-of-class assignments as workforce development tool among department directors and labor representatives (6.1)

• SDHR to collaborate with HR teams to foster buy-in around use of outof-class assignments as a workforce development tool (6.1)

• Form working group to identify target skills for a leadership workforce development training for entry-level employees (6.2)

• Form working group to identify key training goals for a technology and data analytics workforce development program (6.2)

• Form working group, including labor representatives and private sector partners, to develop Civic Tech Fellowship program details (6.3)

Within 3 years:

• Amend personnel policy rule for out-of-class assignments to strengthen workforce development language (6.1)

• Work with Seattle Colleges to launch new leadership and technology workforce development programs to pilot departments (6.2)

• Launch Civic Technology Fellowship program on a pilot basis with one City department (6.3)

Within 5 years:

• Evaluate workforce development pilot results, improve program details, and offer leadership and technology workforce development programs City-wide (6.2)

• Evaluate Civic Technology Fellowship pilot results, improve program details, and increase size of cohort to place fellows in departments across the City (6.3)

7: Creating more Accessible and Equitable Entry-points into City Jobs

While the previous section addressed upskilling strategies for incumbent employees, this section explores how Seattle can create more accessible and equitable entry points into city jobs.

The City of Seattle competes with the private sector for talent and, based on internal reports and interviews, we understand that losing employees to the private sector is prevalent. The City should partner with Career Connect Washington to cultivate interest in public sector jobs among youth to highlight its position within a

Barriers to Entry into City Jobs

As the report’s earlier RSJEI overview section detailed, underrepresentation is prevalent among women, multiracial, and Latinx individuals, though the City’s workforce is otherwise generally representative of Seattle’s population.

Government jobs are often construed as unattainable, especially for underrepresented communities who may think they do not have the social and political capital to effectively navigate the city’s hiring process.

In interviews, City departments and community members expressed concern that those with social connections (FBI’s: friends, brothers, and in-laws) are typically the ones best positioned to obtain city jobs. While there may be relative representative

competitive job market and offer a more compelling workplace image as an employer of choice.

The City should also bolster its RSJEI efforts by hiring a full-time Chief RSJEI Officer, including Race and Social Justice change agents in hiring committees, and tracking the entire hiring process. The City should also pilot a competencies-based hiring system that assesses an applicant’s skills, abilities, and knowledge rather than relying primarily on degrees and credentials.

diversity in the City’s workforce, especially in entry-level jobs, access to these jobs could be more equitable.

Organizations with a workforce reflective of the people they serve tend to outperform those who do not.99

Recognizing this, the City has been making intentional and strategic investments in their workforce to help remove structural and institutional barriers, and create a more inclusive work environment. This section builds on the City’s commendable RSJEI and workforce equity efforts.

Importantly, any changes the city makes should be in coordination with stakeholders, including labor unions, workforce training boards, schools, and other community organizations

Recommendation 7.1: Partner with Career Connect Washington to cultivate a public sector pipeline starting in high school

The City should strengthen its public sector talent pipeline to be competitive for a future workforce and increase equitable entry points into city jobs. The City recognizes it is struggling to attract talent because of changing workforce expectations, outdated human resource practices, and its inability to relay a compelling story about its mission-driven work.100

Government jobs are often able to attract talent, despite paying salaries below the private sector on average, due to employees’ commitment to public service.101 Interested in becoming an Employer of Choice and expanding its marketing strategies to spark interest in public sector jobs, the City could benefit from conducting outreach about city jobs to residents as early as adolescence to help paint a more captivating image of local government work and help inspire interest in the public sector as a dynamic career track.

We recommend that the City partner with Career Connect Washington to expose high school students to career opportunities in the public sector.

Career Connect Washington is a public-private partnership that exposes Washington K-12 students to high indemand industries in the state through work-based learning opportunities. We

believe that such a partnership can help the City position itself as a competitive employer and help expose students to opportunities in the public sector.

There are three types of partnerships available, with varying degrees of commitment from participating stakeholders:

• Career Awareness and Exploration: Students are exposed to local government jobs through career fairs, worksite tours, career presentations, and job shadowing. This would expand on the City’s existing outreach efforts.

• Career Preparation: Students are given hands-on experience in particular fields through internships and instructional work-based learning, which allows them to make more informed decisions about which type of training and educational opportunities to pursue.

• Career Launch: Students combine paid work experience with aligned classroom learning to gain credentials and be a competitive job candidate.

Appendix C further details the three options for Career Connect Washington partnerships.

Recommendation 7.2: Appoint a Race and Social Justice, Equity, and Inclusion Officer (RSJEIO) with clear and enhanced authority

The City has several RSJEI leaders across city departments, but it does not employ a full-time RSJEIO 102 Chief Diversity Officer and Chief Equity Officer titles are typically used for these types of positions, however, RSJEIO will be used in this section to reflect the more comprehensive view of the role’s intent. The City should appoint a RSJEIO with the following in mind

The RSJEIO should have clear and enhanced authority over the City’s various RSJEI efforts, including workforce equity, service delivery, and program implementation.

While some cities hire a RSJEIO primarily to increase diverse hires in a city’s workforce, Seattle should fully embrace an equity leader who can also examine systemic challenges and provide policy solutions. The RSJEIO could work in coordination with the City’s Director of Workforce Equity and the Race and Social Justice Initiative (RSJI) leadership to further strengthen the City’s RSJEI efforts, including tracking data to monitor progress on the city’s various initiatives and reporting outcomes under unified coordination.

The RSJEIO should be appointed to a cabinet-level post.

Having direct access to, and the backing of, the Mayor can be useful for expediting decision-making and more efficiently gaining traction on RSJEI initiatives. Cities across the U.S., including Buffalo, NY and Chicago, have appointed cabinet-level chief

equity officers and have improved RSJEI outcomes within each city’s identified goals.103

Organizations that have hired a RSJEIO have experienced not only an increase in the number of hires from underrepresented communities, but also improved productivity, retention, and morale among their employees.104 Cities with RSJEIOs, such as Philadelphia and Columbus, agree that having an individual fully dedicated to a city’s RSJEI efforts can increase accountability in tracking progress and effectively streamlining goals and resources.

Though every city differs on their bureaucratic challenges and resource limitations, most RSJEIOs in local government unanimously agree that there are three important factors that can better drive the success of a city’s diversity and inclusion efforts: 1. Leadership’s commitment to the issues, 2. Having a plan that evolves as the city addresses the issues, and 3. A city’s willingness to leverage resources to fund RSJEI efforts. The City’s commitment to the first two could be better complemented by further investing in the third key ingredient.

One person cannot do this work alone. Creating any change around RSJEI issues requires the commitment of an array of stakeholders, from the Mayor to the departmental leadership staff and local communities. Hiring a RSJEIO strengthens the work, but should not

replace the additional coordination and resources needed to push forward equitable policies within the City.

Recommendation 7.3: Track the stages of the hiring process

In interviews, City employees and community members expressed concern that the city hiring process can be cumbersome and inconsistent (for example, “FBI hires”), which can prevent underrepresented applicants from moving through the hiring process.

The City’s workforce development strategies include expanding outreach and recruitment opportunities, especially for underrepresented communities, yet there is little data to track progress on these efforts.

Even if the City invests in significant resources for recruitment and outreach, and actually increases the number of underrepresented applicants applying, these applicants may not make it to the interview phase if their application does not move forward.

Based on interviews with city staff and our team’s assessment, we recommend that the City track the

entire hiring process, and in particular, evaluate whether there are discrepancies between the various phases of the hiring process.

Hiring funnel frameworks have been used by private employers to attract and retain a talented and diverse workforce. The model closely monitors applicants in five stages to build a desired workforce: recruitment, application submitted, interview, job offer, and actual hires.105

This model allows employers to better identify where they lose applicants in the process and how they can redirect their hiring strategies. In addition to tracking data, the City should also ask qualitative questions to better identify barriers to entry at each particular hiring phase.

Recommendations 8.1 and 8.2 in the next section further address updates to the city’s data systems.

Recommendation 7.4: Include RSJI change agents in the interviewing process

The City currently does not mandate diverse hiring committees, though it recognizes the importance of the practice for improving RSJEI.106

As part of its employment pathways and workforce diversity plans, the City has instituted a Minimize Bias in

Employment Decisions (MBED) program that trains managers, supervisors, and employees involved in employment decisions with strategies to recognize and minimize bias in their decision-making. These individuals include those involved in the

recruitment, hiring, promotion, and disciplinary process of city employees.

These trainings are commendable, and could be complemented by the addition of diverse City employees in the actual hiring committees.

As discussed in Section 4, during the RSJI Conference, members from our team had the opportunity to interview RSJI change agents about their roles within the City’s race and equity efforts. This cohort of city employees volunteers their time to provide leadership development trainings and facilitate critical dialogue on anti-racism strategies within their departments. While change agents are proud of the work they have done, they also expressed concern that their roles do not carry enough enforcement authority within departments.

Given our interviews with city employees and leading literature on best hiring practices to strengthen RSJEI efforts, we recommend that the City include RSJI change agents in the interviewing process. These change agents are already spearheading much of the work to create an office culture of

awareness and understanding around race and social justice issues in city departments, and are in a position that allows them to understand the competencies that are needed to strengthen their department’s culture and workplace environment.

Change agents could work with their department’s hiring managers to review the list of qualifications being sought, ensuring that RSJEI competencies are a part of the hiring process. These competencies could include previous work experience working with diverse communities, an understanding of systemic racism and barriers for people of color, and open-mindedness to better understanding these issues.

Including change agents in the hiring process has the potential to increase the number of underrepresented individuals hired, inspiring confidence among applicants that they are applying to work in a place that is representative of the City’s residents and understands their unique backgrounds and set of challenges. Change agents, for their part, will also feel empowered that they can further impact the City’s RSJEI efforts.

Recommendation 7.5: Pilot a competencies-based hiring program in a city department

A recurring concern among City employees and community members was that City job postings have extensive requirements that prevent people from applying. These requirements create barriers to entry, which can hamper RSJEI efforts.

A better approach is competencybased hiring, which focuses on the ability of workers to display behaviors, knowledge, abilities, and personal characteristics, rather than relying on their years of work experience or degrees and credentials. For example, when considering an applicant for a

customer service role, a hiring manager can evaluate an applicant’s social perceptiveness, responsiveness to customer needs, and problem solving.

The City of Albuquerque piloted a competencies-based hiring system for entry-level positions for their city jobs. They found that while only 1% of the city’s population qualified for jobs with a college degree requirement, 33% qualified for the same jobs when assessed on competencies. Within two years, the City of Albuquerque added more than 500 people into their city workforce through this approach.107

Additionally, the Canadian Public Service system has also employed competencies-based hiring in order to increase the hires of underrepresented populations. Importantly, they have complemented this type of hiring with a comprehensive RSJEI plan that seeks to build a more inclusive pipeline as diverse workers up-skill from entry-level jobs.108

The City has previously documented the benefits of this type of hiring system, especially for backfilling entrylevel positions and proactively addressing succession planning. Nonetheless, there are likely significant

resource and time constraints associated with a complete overhaul of Seattle’s current job postings.

Based on best practices and our team analysis, we recommend the City pilot a competencies-based hiring program in one department, allowing the City to test this approach at a small scale. A pilot in one department can assuage concerns among skeptics regarding the value of such a hiring system, can help keep costs low, and can help finetune the process in one department for others to adopt later more expeditiously.109

As a starting point, the City can consider the following characteristics when identifying a department:

• Has enough entry-level competencies that can scale across other departments

• Hires a significant number of people every year

• Small enough, about 100-150 employees, that it can implement the pilot in the near future

Possible departments to consider include the Department of Human Resources or Department of Education and Early Learning.

Implementation Timeline

Within 12 months

• Begin partnership with Career Connect Washington starting with the Career Awareness and Exploration track (7.1)

• Appoint a Race and Social Justice, Equity, and Inclusion Officer (7.2)

• Begin review and planning for a framework to track the hiring process, including data systems and qualitative assessments (7.3)

• Have hiring managers in each department coordinate with RSJI change agents to include them in the interviewing process (7.4)

• Identify one city department for competencies-based hiring pilot and try to begin implementation

process (more detailed timeline of steps in earlier section) (7.5)

Within 3 years

• Add second track, Career Preparation, to partnership with Career Connect Washington (7.1)

• Implement framework to track hiring process (7.3)

• Full implementation of competencies-based hiring pilot in one city department and collection of data on pilot results (7.5)

Within 5 years

• Add third track, Career Launch, to partnership with Career Connect Washington (7.1)

• Consider expanding competenciesbased hiring to City hiring (7.5)

8: Preparing the City’s Workforce Management Systems for the Future

For the City to better prepare its workforce for the future, we have provided recommendations on innovation, workforce development, and recruitment. However, for these recommendations to be implemented successfully, the City will also need to change the systems it uses to manage its workforce.

Many of the challenges with these systems are already well known to the City’s staff and leaders. These challenges will only grow in coming years as technology and innovation disrupt the way the City operates.

This section of the report outlines a way forward for the City as it considers how to meet these challenges. In general, the City should ensure its systems:

• Provide rich and accurate data for workforce analysis

• Provide improved flexibility to adapt to changing needs

• Reduce complexity and scope for biases and special treatment

While improving these systems will come at a cost, investing in these changes now is an essential step for the City to ensure it is operating on a foundation that is fit-for-purpose.

Hiring and staff management software

All City departments use NeoGov, a third-party platform, to manage hiring processes. Employees are then integrated into EV5, the City’s Human Resources Information System (HRIS) software.

Using the same software across the City provides a basis for aligning data and management across the City. This is further supported by centralized expertise in SDHR and SDIT.

However, throughout our research we learned about the challenges that these systems are presenting – challenges that will only grow in coming years.

The City continues to use outdated versions of software, creating risks.

Using outdated versions of EV5 and other systems limits functionality and creates risks around security and maintenance 110 A fault in staff management or hiring software could present a major problem for the City’s human resources operations.

Several departments were also concerned that the NeoGov interface is difficult for applicants to navigate, presenting a barrier to employment, particularly for underrepresented groups. Our experience navigating NeoGov supports this concern.

A lack of functionality is also limiting workforce analysis and planning.

In interviews with departments, we heard that NeoGov provides limited data on applicant pools or the progress of applicants through the stages of the hiring process. It also fails to track individuals who apply for multiple positions or identify past applicants who may be suitable for new postings.

NeoGov and EV5 are also fundamentally separate systems, preventing easy matching of existing staff to new opportunities with the City.

The Future of Work will only exacerbate these challenges.

Any barriers to entry – even a complicated job portal – could hamper the City as it competes for talent.

In addition, as the skills needs of the City change rapidly, the City will need rich data on the skills of its existing workforce, and the depth and diversity of the talent pool.

As technology and innovation change the City’s workforce needs over time, it will also be vital for the City to have effective systems for matching affected staff to other roles and vacancies.

Indeed, without improvements in these important pieces of software, it may be impossible to implement some of the recommendations in this report, such as Recommendation 7.3: Track the stages of the hiring process.

Recommendation 8.1: Review and update all critical City software programs

Continuing to use legacy software in any critical function creates risks to the City’s capacity to deliver services.

In addition, for the City to be a model in the adoption of technology and innovation, it must begin by modelling

this approach in the fundamentals of its own management.

We recommend the City immediately review all critical software programs to ensure they are up-to-date.

Recommendation 8.2: Investigate and adopt alternatives to NeoGov and EV5

We understand that alternatives to NeoGov are currently being explored a process that could be expanded to include EV5. Any new software should provide a simplified user experience for applicants and staff, and rich data on

skills and diversity for leaders to use for workforce planning and management.

We recommend the City identify new software that can provide this functionality and ensure a new system has been adopted within three years.

Classifications

The City uses a system of highly prescriptive classifications (job titles) to categorize staff.

These classifications convey information including the main job type (such as ‘Police Officer’ or ‘Accountant’), specifics about a particular role, seniority and pay levels, and department or team location.111

Classifications are intended to serve an equity purpose by ensuring that, if an employee’s position is abolished, they can be ‘bumped’ to another position with the City that matches their classification.112

However, over time the classification system may have created equity problems of its own.

Classifications have not been updated since 1991, preventing workforce analysis and planning.

Since then, the tasks done by the City’s workforce have changed dramatically, as have the skills employees need.

At the same time, as departments have added new classifications for different roles, rankings, and pay levels, the number of classifications has proliferated to more than 1,000.

Many distinct classifications may exist for a reason – but the complexity of the system presents a further barrier to workforce planning. The proliferation of classifications may also reflect incentives to create new roles for specific staff (see Box 8.1).

Box 8.1: The proliferation of strategic advisor classifications

Over time, pay rules have led departments to hire specialized policy and technical staff under the broad term ‘strategic advisor’. Staffing data provided by the City reveals there are now 793 permanent and 108 temporary staff employed using this title– 6% of the City’s workforce.

However, there are 56 different variations of the ‘strategic advisor’ classification in use. Some of these are duplicative, such as ‘Strategic Advisor – General Government’. Others identify expertise, such as ‘Finance, Budget and Accounting’.

Others still are extremely narrow, specifying level, expertise and department, and account for only a handful of employees. In these cases, it is possible that new classifications have been created for a specific role or even a specific employee.

There are benefits to having some flexibility to create specialized roles to match specific needs. However, there are risks if this creates opportunities for discrimination or favoritism. At the same time, it is unlikely that the broad term ‘strategic advisor’ accurately captures the main functions performed by 900 different staff across the City, making these classifications of little use for workforce planning. Importantly, this is just one example of a problem that is evident across many different classifications used across the City.

Classifications intersect with an equally complex pay structure, which may limit mobility.

Each classification relates to a pay ‘grade’ with a series of stepped increases (or a more flexible pay range for some classifications).113

There are 130 starting grades, ranging from $10 per hour to $120 per hour, and each contains five upwards steps, overlapping with subsequent grades.

Pay differences reflect the result of negotiations between management, staff, and unions. However, the complexity and narrow definition of roles and pay grades limits the scope

for junior staff to progress in pay and seniority without changing classification.

The City will need greater flexibility as work continues to change.

New technologies and changes to service delivery models will mean that existing roles will change in coming years, and new roles will need to be created that currently do not exist.

This means that the City not only needs a classification system that satisfies current demands, but one that will continue to adjust in line with workforce needs.

Recommendation 8.3: Develop a new set of job ‘categories’ that can be updated over time

The City should review and update job classifications to reflect the current ‘categories’ of work done by staff. Categories should be consistent across departments where possible, such as ‘policy adviser’ and ‘program manager’.

In addition, the City should implement a systematic approach to updating job categories going forward, for instance

by drawing updated information on tasks and skills from job ads or performance evaluations.

Development of these categories should be led by a working group of human resources staff from across departments, in conjunction with union representatives, department leaders, and staff.

Recommendation 8.4: Explore options to simplify the pay structure within categories

The connection between pay grade and classification underpins the complexity and rigidity of the current system.

With updated job categories in place (Recommendation 8.3), the City should explore options to simplify the pay structure for each category.

For example, each of the new job categories could overlay a number of pay grades, reflecting possible promotion steps for these employees.

It may also be valuable to simplify the number of pay grades and eliminate existing overlaps.

Where the current system specifies a unique classification for each intersection of role and pay grade, a simplified system could allow staff to be promoted to a higher grade while remaining in the same job category, providing more upward mobility as well as flexibility for management.

Implementing these kinds of changes will be complex and time-intensive, and impacts on staff will need to be considered carefully.

Relevant stakeholders including the Seattle Budget Office, SDHR, and

Positions and staffing budgets

The City’s classification and pay grade system is directly linked to departments’ budgets through the position system.114 Each department is allocated positions (‘pockets’) for staff in a specific classification, with associated funding.

The position system can distort the workforce and limit opportunity.

We heard from multiple departments that the position system limits their ability to create entry level jobs. For example, departments cannot hire new apprentices unless there is a vacant position that could be filled by an apprentice upon completion of training.

Some departments may also try to retain more senior positions to avoid losing the associated funding, even if these positions are no longer needed.

unions, should work together to explore whether these kinds of changes could be advantageous for the City and its workforce.

Limits on permanent positions may also push departments to rely more on temporary and contract staff, with negative consequences for both job security and retention rates.

Fostering a culture of innovation will require more flexibility.

Recommendation 5.1 of this report suggests the City embrace a ‘laboratory’ approach to innovation.

Making this a reality will require department leaders to have the flexibility to experiment with new programs and initiatives – and scope to adjust staffing accordingly.

More broadly, as technology reshapes the way services are delivered, leaders will need to be able to shape their workforce to meet future needs.

Figure 8.1 presents an illustrative example of how this kind of system could function.

Figure 8.1: Illustration of proposed separated job categories and pay grades

Recommendation 8.5: Test the allocation of total FTE rather than specific positions

Allocating each department a total number of full-time equivalent staff (FTE), rather than specific positions, would be one way to provide leaders with more flexibility to structure their workforce according to need.

Staffing budgets could be funded based on the average cost-per-employee across the City, allowing departments

Implementation timeline

Within 12 months:

• SDIT to review and update critical software (8.1)

• SDHR and SDIT to begin investigation of alternatives to NeoGov and EV5 (8.2)

• Working group formed to update classifications (categories) (8.3)

Within 3 years:

• Implement an integrated hiring and HRIS system with improved functionality (8.2)

• SDHR and Budget Office to begin exploring options to simplify pay structure, in conjunction with unions (8.4)

• SDHR and Budget Office to begin trial of total FTE allocation with a small department (8.5)

to hire entry-level staff without impacting their overall budget allocation.

Given the scale of this kind of change, the City should test this approach with one or more small departments to determine its effects on staffing, budget systems, and accountability.

Within 5 years:

• Implement updated classifications (categories) (8.3)

• Finalize and agree a path forward on pay structure with unions (8.4)

• Review FTE allocation trial and determine process for City-wide rollout if successful (8.5)

9: Conclusion

This report focuses on the advances in digital technology (artificial intelligence, machine learning, big data and automation) that are transforming the nature of work. Specifically, the report identifies macro-level trends that will affect the City of Seattle’s municipal workforce in coming years, and our recommendations for responding to these trends.

We recommend that the City embrace technologies and change to improve its operations. By doing so, the City can increase employees’ productivity, provide new ways to monitor and evaluate the effectiveness of programs and policies, and improve the quality of service delivery for the community.

Nevertheless, the City must be intentional about the deployment of digital technologies. In particular, we find that as such technologies are adopted, the tasks of many current staff may be disrupted. The demand for new skills may also present challenges as the City continues to support RSJEI efforts in its workforce.

Implementing a “City as Lab” approach will allow departments to iterate on

pilots to ensure that innovations respond to user needs and can be implemented effectively and equitably.

In turn, harnessing a worker-centric approach by engaging relevant stakeholders (e.g. unions), investing in workforce training, and improving recruitment processes is critical for fostering sustainable transitions. Taking proactive steps to promote equitable opportunities is also key to ensure that the City mitigates potential disparate impacts on its workers.

Finally, we recommend that the City update legacy systems and other underlying processes in its workforce management infrastructure.

The graphic overleaf summarizes the team’s specific recommendations, and associated implementation timelines, to the City.

By taking these steps, we believe the City of Seattle can be a model for technology adoption in the public sector, with a workforce well positioned to thrive in the future of work.

Combined implementation timeline

Next 12 months

2020

Implement simple, low resource recommendations

Begin preparations for more significant recommendations

Next 3 years

2021-2022

Complete implementation of intermediate recommendations

Begin pilots and tests of the most significant recommendations

Major milestones:

• Guidelines for laboratory approach established (5.1)

• Chief Data Officer role established (5.3)

• ‘Out-of-class’ memo distributed and periodic ‘Opportunity Email’ launched (6.1)

• Partnership with Career Connect Washington established (7.1)

• Appoint a Race and Social Justice, Equity, and Inclusion Officer (7.2)

• RSJI change agents included in interviewing process (7.4)

• Critical software reviewed and updated (8.1)

Major milestones:

• Laboratory approach in place and first pilots underway (5.1)

• Broader IAC rubric in place and expanded Internal Review Board purview in effect (5.2)

• ‘Out-of-class’ personnel policy rule amended (6.1)

• New leadership and technology training programs launched (6.2)

• Civic Tech Fellowship program launched (6.3)

• Career Connect Washington partnership expanded (7.1)

• Competencies-based hiring pilot underway (7.5)

• Integrated hiring and HRIS system in place (8.2)

• Total FTE allocation pilot underway (8.5)

Next 5 years

2023-2024

Finalize and agree to remaining changes and decisions

Evaluate pilots and tests, and determine path to scale

Next 10 years

2025-2029

Continue to innovate and embrace new and emerging technologies, while supporting a diverse and effective workforce

Major milestones:

• First tech pilots evaluated and pipeline expanded (5.1)

• Leadership and technology training evaluated (6.2)

• Civic Tech Fellowship program evaluated (6.3)

• Career Connect Washington partnership expanded to highest-resource track (7.1)

• Regular analysis of hiring process data occurring (7.3)

• Competencies-based hiring pilot evaluated (7.5)

• Job classifications (categories) updated (8.3)

• Path forward on simplified pay structure agreed (8.4)

• Total FTE allocation pilot evaluated (8.5)

Major milestones:

• Successful new technology and service delivery pilots rolled out city-wide, with a culture of innovation established

• Workforce technology competency and leadership skills improved, ensuring smooth adjustment to changes

• Continued improvement in, RSJEI goals, including upward mobility for entry-level staff

• Management systems improved, allowing for effective workforce planning and flexibility for management

Appendix A: Data Analysis

Specifications

This report features four key regressions to analyze the City’s employment data:

Specification 1:

The model selection procedure was straightforward. We began with a quartic in age, which is a standard specification used in labor market analysis to control for experience. Usually, one earns more money over time at a decreasing rate, requiring at least a quadratic in age. Then, earnings sharply decline into retirement, requiring two additional terms.

We tried different combinations of ����� and ������� and stopped adding powers of ����� and ������� when their coefficients became insignificant according to a standard t-test with homoscedastic error. The key parameter in the above regression is �0 , which is approximately (up to the standard first order Taylor approximation) the experience-adjusted gender wage gap. We might be concerned that women sort into different departments with different returns to experience, prompting further specifications.

Specification 2:

So that �0 is now the experience and department adjusted wage gap. The arts and culture department is the excluded category. We might be concerned that the wage gap is localized to specific departments, which motivates the final sex-wage gap specification we test.

Specification 3:

Now, every department has a specific sex wage gap, computed by: �0 + ����G �I where ����G is the department in question (e.g. Mayor’s office, Fire Department etc.). The omitted variable is the arts department. So, we could say that a female in the arts department earns �0 % more than man (note that �0 ) will be negative. We find

that the wage gap for most departments and the “conditional main effect” �0 is insignificant. That inspires the following, final regression specification.

In this regression, we dropped all departments with less than 25 people in them. We have only retained 10 department interactions. When we compare this regression to Specification 3, used a nested likelihood ratio test, we find that the 17 remaining interactions are not collectively statistically significant. We find basically the same result without dropping all departments with less than 25 people in them but noisier coefficients. This suggests that the sex gap is concentrated to only a few, large departments.

When we analyze racial gaps, we begin with our second specification and add a race coefficient.

The reference category is white so that the gap between race j and whites is �b . We do not attempt to interact race with department because of the large number of parameters we would be estimating. SPECIFICATION 6 drops the department controls.

For all specifications except 3 we assume heteroskedasticity. We use Specification 3 primarily for exploratory purposes, so we report it with homoscedastic errors. For the regressions including Departments, we analyzed both standard heteroskedastic errors and standard errors clustered by Department. Because unionization agreements take place between as well as within Departments, we are skeptical that the assumption of independent clusters is satisfied. We present heteroskedastic standard error results below and in the main report.

Regression Tables

Specification 1

Specification 2

Dependent

Specification 3 (Homoskedastic Error Assumed)

Dependent

Specification 4

Dependent

I(CityExperience3) 0.0001*** (0.00004, 0.0001)

I(CityExperience4) -0.00000** (-0.00000, -0.00000)

SEXF:police -0.220*** (-0.245, -0.195)

SEXF:lawdept -0.221*** (-0.302, -0.139)

SEXF:citylight -0.161*** (-0.188, -0.133)

SEXF:earlylearn -0.206*** (-0.327, -0.085)

SEXF:muni -0.174*** (-0.238, -0.110)

SEXF:construction -0.147*** (-0.190, -0.105)

SEXF:center -0.115*** (-0.155, -0.074) Constant 1.604*** (1.420, 1.788)

Note: *p<0.1; **p<0.05; ***p<0.01

Specification 5

Dependent variable:

Specification 6

Dependent variable:

Appendix B: List of Stakeholders

Interviewed

Name Organization

Title

Dave McFadden Port of Seattle Economic Development Managing Director

Jai Elliott Seattle City Light Talent Acquisition and Workforce Development Director

Dwayne Chappelle Seattle Department of Education and Early Learning Director

Rachael Tatman Kaggle Data Scientist

Suzan Levine Washington Employment Security Department Commissioner

Jeanne Fulcher Priority Hire Labor Equity Program

Jesús Aguirre Seattle Parks and Recreation Superintendent

Christopher Williams Seattle Parks and Recreation Deputy Parks Superintendent

Eleni Papadakis WA Workforce Training and Education Board Executive Director

Sam Zimbabwe Seattle Department of Transportation Director

Sherysse Morris Seattle Department of Transportation Director of Human Resources

Joshua Winter Microsoft Philanthropies Director, Skills and Employability

Brenda Wiest Teamsters Local 117 Vice President & Legislative Director

Bobby Lee Seattle Office of Economic Development Director

Nancy Yamamoto Seattle Office of Economic Development Director, Workforce Development

Matthew Houghton Seattle Office of Economic Development Workforce Development Adviser

Akhtar Badshah University of Washington Distinguished Practitioner

Mami Hara Seattle Public Utilities Director

Debra Smith Seattle City Light Director

Jai Elliot Seattle City Light Talent Acquisition and Workforce Development Director

Robert Nellams Seattle Center Director

Name Organization

Title

Aparna Rae Future for US Co-Founder

Sage Quiamno Future for US Co-Founder

Malcolm Grothe Seattle Colleges

Andrea Caupain Byrd Barr Place

Associate Vice Chancellor for Workforce and Economic Development

Chief Executive Officer

Saad Bashir Seattle IT Department Chief Technology Officer

Kristen Fox Swedish Hospital Regional Chief Human Resources Officer

Jason Johnson Human Services Department Interim Director

Leah Tivoli Seattle Innovation & Performance Manager

Julia Reed Seattle Mayor's Office

Senior Policy Advisor

Dennis Mortensen x.ai Chief Executive Officer

Eli Dvorkin Center for an Urban Future Editorial and Policy Director

David Ehrenberg Brooklyn Navy Yard President and Chief Executive Officer

Joseph Morrisroe NYC 311

Executive Director

Yasmin Ali Skillspire Founder and Executive Director

Appendix C: Career Connect Washington

Career Connect Washington is a public-private partnership that exposes Washington K-12 students to indemand industries in the state through work-based learning opportunities. We believe that partnering with Career Connect Washington can help the City position itself as a competitive employer and help expose students to opportunities in the public sector.

There are three buckets of partnerships available, with varying degrees of commitment from participating stakeholders.

Career Awareness and Exploration

Students are exposed to local government jobs through career fairs, worksite tours, career presentations, and job shadowing. Widely cited literature on 21st century career preparation recommends offering worklinked learning opportunities to students beginning in middle school and no later than high school.115 Exposing younger students, in particular students from underrepresented communities, to career opportunities helps them understand the types of tasks and challenges associated with certain fields, especially those that might seem unattainable to them.

The City’s Workforce Equity team recently launched the City’s first Public Sector Diversity Career Fair, which attracted over 400 diverse adult jobseekers. The team has additionally attended four diversity focused career

fairs and community events. We believe that the City can expand on these efforts by starting outreach in high schools and creating exposure and opportunities at an earlier age. The earlier the City engages with students, the sooner students can be exposed to careers in the public sector.

Career Preparation

Students are given hands-on experience in particular fields through internships and instructional workbased learning, which allows them to make more informed decisions about which type of training and educational opportunities to pursue. The Organization for Economic Cooperation and Development has found that workbased learning is the most effective method in helping prepare the next generation of workers, particularly lowincome and low-skilled youth who already face multiple barriers to entry into the labor market, by providing a structure to support the transition from school to the workplace.116

Most of the City’s current workforce talent pipeline efforts are catered to students enrolled in post-secondary educational programs.117 Existing internship opportunities with the City are typically short-term and could be enhanced to increase employment pathways into permanent City jobs. For example, the City could host a determined number of high school students per department and engage them in meaningful projects to give them a taste of public service.

Career Launch

Students combine paid work experience with aligned classroom learning to gain credentials and be a competitive job candidate. While rare in the United States, countries such as Switzerland and Germany have benefitted from this model of career preparation (typically through apprenticeships) for their youth.118 Career Connect Washington is one of the first states to explore this career pathway track, and we believe Seattle could benefit from partnering as an employer for this proven career exploration model.

This is the most intensive path, and can be part of a long-term investment from the City. This would likely involve higher City funding for these paid positions

and time commitment to work on curriculum development.

We think these partnerships can help create more accessible entry-points into city jobs at an earlier age, through earlier connections to city employees, mentoring opportunities, and exposure to the inner workings of city government work. The high school pipeline is particularly helpful to underrepresented students who are less likely to have access to social networks that can help them gain exposure to careers in city government. The City can benefit from building longer-term investments in their youth, starting before college, so that pathways into city jobs are more clearly defined and so that Seattle’s future workforce perceives city government jobs to be competitive, meaningful, and engaging.

Endnotes

1 The Future of Jobs 2018- Key Findings. “The Future of Jobs 2018- Key Findings.” The World Economic Forum, September 2018. https://wef.ch/2NHqHKK

2 Ibid

3 “Machine Learning: What It Is and Why It Matters.” SAS Institute Inc. Accessed December 23, 2019. https://www.sas.com/en_us/insights/analytics/machine-learning.html

4 “What Is Automation?- ISA.” The International Society of Automation, n.d. https://www.isa.org/about-isa/what-is-automation/

5 Lund, Susan, James Manyika, Liz Hilton Segel, Andre Dua, Brian Hancock, Scott Rutherford, and Brent Macon. “Future of Work in America | McKinsey.” Electronic. Work in America. McKinsey Global Institute, July 2019. https://www.mckinsey.com/featuredinsights/future-of-work/the-future-of-work-in-america-people-and-places-today-and-tomorrow

6 Autor, David, David Autor, Anna Salomons, and Anna Salomons. "Is Automation Labor Share-Displacing? Productivity Growth, Employment, and the Labor Share." Brookings Papers on Economic Activity 2018, no. 1 (2018): 187. doi:10.1353/eca.2018.0000

7 Lund, Susan, James Manyika, Liz Hilton Segel, Andre Dua, Brian Hancock, Scott Rutherford, and Brent Macon. “Future of Work in America | McKinsey.” Electronic. Work in America. McKinsey Global Institute, July 2019. https://www.mckinsey.com/featuredinsights/future-of-work/the-future-of-work-in-america-people-and-places-today-and-tomorrow

8 “The Automation Roadmap: CMU Professors Use Machine Learning to Forecast Disruptive Tech.” Carnegie Mellon University, March 2018. https://www.heinz.cmu.edu/media/2018/March/automation-roadmap

9 “Future of Work.” City of Seattle. Accessed January 5, 2020. https://performance.seattle.gov/stories/s/Future-of-Work/ujqhe68z/

10 Lund, Susan, James Manyika, Liz Hilton Segel, Andre Dua, Brian Hancock, Scott Rutherford, and Brent Macon. “Future of Work in America | McKinsey.” Electronic. Work in America. McKinsey Global Institute, July 2019.

https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-andtomorrow

11 Davenport, Thomas H., and Rajeev Ronanki. “Artificial Intelligence for the Real World.” Harvard Business Review, January 1, 2018. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

12 Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. “The Future of Patent Examination at the USPTO,” Harvard Business School Case 617-027 April 11, 2017. https://www.hbs.edu/faculty/Pages/item.aspx?num=52478.

13 Lund, Susan, James Manyika, Liz Hilton Segel, Andre Dua, Brian Hancock, Scott Rutherford, and Brent Macon. “Future of Work in America | McKinsey.” Electronic. Work in America. McKinsey Global Institute, July 2019. https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-andtomorrow

14 Fleming, Martin, Wyatt Clarke, Subhro Das, Phai Phongthiengtham, and Prabhat Reddy. “The Future of Work: How New Technologies Are Transforming Tasks,” October 31, 2019. https://mitibm watsonailab.mit.edu/research/publications/paper/download/The-Future-of-Work-How-New-Technologies-Are-TransformingTasks.pdf

15 Lund, Susan, James Manyika, Liz Hilton Segel, Andre Dua, Brian Hancock, Scott Rutherford, and Brent Macon. “Future of Work in America | McKinsey.” Electronic. Work in America. McKinsey Global Institute, July 2019. https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-andtomorrow The authors define middle-wage jobs as those in the middle 40 percent of the income distribution (2017). Note that this analysis does not account for different wage growth or decline over time. High-skill and low-skill jobs are defined based on David Autor and David Dorn, “The growth of low-skill service jobs and the polarization of the US labor market” (American Economic Review, 2013).

16 Muro, Mark, Robert Maxim, and Jacob Whiton. “Automation and Artificial Intelligence: How Machines Are Affecting People and Places.” The Brookings Institution , January 1, 2019. https://www.brookings.edu/wpcontent/uploads/2019/01/2019.01_BrookingsMetro_Automation-AI_Report_Muro-Maxim-Whiton-FINAL-version.pdf

17 The Future of Jobs 2018- Key Findings. “The Future of Jobs 2018- Key Findings.” The World Economic Forum, September 2018. https://wef.ch/2NHqHKK

18 Fuller, Joseph B., Judith K. Wallenstein, Manjari Raman, and Alice de Chalendar. “Future Positive: How Companies Can Tap Into Employee Optimism to Navigate Tomorrow’s Workplace.” BCG, Harvard Business School, May 2019. https://www.hbs.edu/managing-the-future-of-work/research/Documents/Future%20Positive%20Report.pdf

19 The Future of Jobs 2018- Key Findings. “The Future of Jobs 2018- Key Findings.” The World Economic Forum, September 2018. https://wef.ch/2NHqHKK

20 For illustrative scenarios of public sector jobs that can be transformed through human-machine pairing, see Deloitte Insights: Designing Future Government Jobs- A Vision for How to Optimize Human and Technological Potential (https://www2.deloitte.com/content/dam/Deloitte/ec/Documents/public-sector/DI_Govt-jobs-of-the-future.pdf)

21 Muro, Mark, Robert Maxim, and Jacob Whiton. “Automation and Artificial Intelligence: How Machines Are Affecting People and Places.” The Brookings Institution , January 1, 2019. https://www.brookings.edu/wpcontent/uploads/2019/01/2019.01_BrookingsMetro_Automation-AI_Report_Muro-Maxim-Whiton-FINAL-version.pdf

22 Ibid

23 “The Future of Work in Black America.” McKinsey, October 2019. https://www.mckinsey.com/featured-insights/future-ofwork/the-future-of-work-in-black-america

24 Ibid

25 Wladawsky-Berger, Irving. “The Impact of AI on Government Opportunities and Challenges.” WSJ (blog), November 22, 2019. https://blogs.wsj.com/cio/2019/11/22/the-impact-of-ai-on-government-opportunities-and-challenges/

26 “Future of Work in Government | Deloitte Insights.” Deloitte, February 28, 2019. https://www2.deloitte.com/us/en/insights/industry/public-sector/future-of-work-in-government.html

27 America, C. for, n.d. How human-centered is our social safety net? Code for America. URL https://www.codeforamerica.org/news/how-human-centered-is-our-social-safety-net (accessed 1.11.20).

28 McKinsey, 2014. Putting Citizens First: How to improve citizen’s experience and satisfaction with public services.

29 Mckinsey, “Improving the Customer Experience to Achieve Government-Agency Goals | McKinsey,” 2017, https://www.mckinsey.com/industries/public-sector/our-insights/improving-the-customer-experience-to-achieve-governmentagency-goals

30 Ibid

31 Mckinsey, “How US State Governments Can Improve Customer Service,” Mckinsey and Company, 2014.

32 Author’s interview with Seattle center on 11.01.2019

33 Kleinberg, J., Ludwig, J., Mullainathan, S., 2016. A guide to solving social problems with machine learning. Harvard Business Review.

34 Can Algorithms Predict House Fires?. Data-Smart City Solutions. URL https://datasmart.ash.harvard.edu/news/article/canalgorithms-predict-house-fires-990 (accessed 1.11.20).

35 Hillenbrand, K., 2016. Predicting Fire Risk: From New Orleans to a Nationwide Tool [WWW Document]. Data-Smart City Solutions. URL https://datasmart.ash.harvard.edu/news/article/predicting-fire-risk-from-new-orleans-to-a-nationwide-tool-846 (accessed 1.12.20).

36 Faruqui, A., Harris, D., Hledik, R., 2010. Unlocking the €53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy, The socio-economic transition towards a hydrogen economy - findings from European research, with regular papers 38, 6222–6231. https://doi.org/10.1016/j.enpol.2010.06.010

37“Technology Program - Transportation | Seattle.Gov.” Accessed December 14, 2019. https://www.seattle.gov/transportation/projects-and-programs/programs/technology-program

38 ITS Strategic plan 2010-2020

39 Ibid

40 “Urbanalytics.” Accessed January 13, 2020. https://urbanalytics.uw.edu/projects/arrayofthings/

41 Five Ways Chatbots Could Transform Government Services. Data-Smart City Solutions. URL https://datasmart.ash.harvard.edu/news/article/five-ways-chatbots-could-transform-government-services-1033 (accessed 1.11.20).

42 Krishnamurthy, K.C.D. and R., 2017. Chatbots move public sector toward artificial intelligence. Brookings. URL https://www.brookings.edu/blog/techtank/2017/06/02/chatbots-move-public-sector-towards-artificial-intelligence/ (accessed 1.11.20).

43 3-1-1, 2019. . Wikipedia.

44 Federal News Network. “New Patent or Not? USPTO Builds AI Tools to Help Employees Decide,” July 26, 2019. https://federalnewsnetwork.com/artificial-intelligence/2019/07/new-patent-or-not-uspto-builds-ai-tools-to-employees-decide/

45 Japan trials AI for parliament use, 2016. . GovInsider. URL https://govinsider.asia/innovation/japan-trials-ai-for-parliament-use/ (accessed 1.11.20).

46 Can Algorithms Predict House Fires?. Data-Smart City Solutions. URL https://datasmart.ash.harvard.edu/news/article/canalgorithms-predict-house-fires-990 (accessed 1.11.20).

47 Ibid

48 Sam Corbett-Davies and Goel Sharad, “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning,” 2018.

49 Julia Angwin, Surya Mattu, and Lauren Kirshner, “Machine Bias: There’s Software Used across the Country to Predict Future Criminals. And It’s Biased against Blacks.,” ProPublica (blog), 2016.

50 National Institute of Standards and Technology, 2019. Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects.

51 Glaser, A., 2019. Do Tech Companies Really Need to Snoop Into Private Conversations to Improve Their A.I.? Slate Magazine. URL https://slate.com/technology/2019/08/big-tech-ai-conversation-snooping-siri-alexa-google.html (accessed 1.11.20).

52 The US House Committee on Energy and Commerce has been conducting regular hearings on data privacy and collection. There has been similar regulatory attention across the globe.

53The City of Seattle, 2018. Open Data Plan 2018. The City of Seattle.

54 Privacy Reviews of City Technology - Tech | seattle.gov URL https://www.seattle.gov/tech/initiatives/privacy/privacy-reviews (accessed 1.11.20).

55 Winston, A., 2018. Palantir has secretly been using New Orleans to test its predictive policing technology [WWW Document]. The Verge. URL https://www.theverge.com/2018/2/27/17054740/palantir-predictive-policing-tool-new-orleans-nopd (accessed 1.11.20).

56 Anteby, Michel, Elena Corsi, and Emilie Billaud. "Automating the Paris Subway (A)." Harvard Business School Case 413061, September 2012. (Revised May 2013.)

57 “Seattle City Light - Advanced Metering | Project Overview.” Accessed December 15, 2019. https://www.seattle.gov/light/ami/

58 The City of Seattle, 2017. City of Seattle Privacy Impact Assessment. The City of Seattle.

59 “Privacy - Tech | Seattle.Gov.” http://www.seattle.gov/tech/initiatives/privacy

60 U. S. Census Bureau. “American FactFinder - Results.” Accessed December 22, 2019. https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

61 “Workforce Equity Update Report.” Seattle, WA: Seattle Department of Human Resources and Seattle Office of Civil Rights. Accessed December 21, 2019. https://www.seattle.gov/personnel/resources/pubs/2019_WFE_Update_Report_v5_Final.pdf

62 We also found evidence of a city-wide, experience-adjusted sex wage gap. In a regression model, we find that adding a number of department-specific controls covering less than half of the city workforce renders both the "main effect" of gender and all excluded departments collectively, statistically insignificant. Because controlling for job titles eliminates any sex wage gap, this is most consistent with women sorting into both lower wage occupations as well as lower wage departments, an issue many cities struggle with. We also find evidence of several Department-specific gender wage gaps, most of which appear to be driven by

The Future of Work for the City of Seattle

occupational sorting. Similarly, our analysis does not show significant differences in representation for people of color (POC) and white employees compared to the city’s population, but we do find evidence of a race wage gap.

63 U. S. Census Bureau. “American FactFinder - Results.” Accessed December 22, 2019. https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

64 We define statistically significant as heaving less than a 1/20 probability of occurring by chance.

65 Office of the Mayor. “Seattle Mayor-Elect Jenny Durkan Announces Seattle’s Most Diverse, Inclusive Transition Committee Representing All Parts of the City,” November 14, 2017. https://durkan.seattle.gov/2017/11/seattle-m ayor-elect-jenny-durkanannounces-seattles-most-diverse-inclusive-transition-committee-representing-all-parts-of-the-city/

66 Government Alliance on Race and Equity. “Advancing Racial Equity and Transforming Government: A Resource Guide,” October 22, 2015. https://www.racialequityalliance.org/2015/10/22/resource-guide/, 29.

67 “Race and Social Justice Initiative - RSJI | Seattle.Gov.” Accessed December 22, 2019. https://www.seattle.gov/rsji

68 Government Alliance on Race and Equity, 23.

69 “Race Equity and Inclusion Action Guide.” The Annie E. Casey Foundation, January 8, 2015. https://www.aecf.org/resources/race-equity-and-inclusion-action-guide/, 5.

70 “City of Seattle Employer of Choice Initiative.” Seattle, Washington: Seattle City Leadership Academy, June 12, 2019.

71 Government Alliance on Race and Equity, 23.

72 NPR.org. “Remembering When Driverless Elevators Drew Skepticism.” January 2017 Accessed December 23, 2019. https://www.npr.org/2015/07/31/427990392/remembering-when-driverless-elevators-drew-skepticism

73 The Financial Times calculator is based on a 2017 McKinsey Report on automation.

74 Madgavkar, Anu, James Manyika, Mekala Krishnan, Kweilin Ellingrud, Lareina Yee, Jonathan Woetzel, Michael Chui, Vivian Hunt, and Sruti Balakrishnan. “The Future of Women at Work: Transitions in the Age of Automation.” McKinsey Global Institute, June 2019.

https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Gender%20Equality/The%20future%20of%20women%20a t%20work%20Transitions%20in%20the%20age%20of%20automation/MGI-The-future-of-women-at-work-Report-July2019.ashx

75 Ibid.

76 The number of regular (non-temporary) municipal positions grew by 2.4% since 2016. The number of regular (non-temporary) information technology positions (proxied by counting all positions that have "info" in their job title) grew by about 7.0% over the same period.

77 de Brey, Cristobal, Lauren Musu, Joel McFarland, Sidney Wilkinson-Flicker, Melissa Diliberti, Anlan Zhang, Claire Branstetter, and Xiaolei Wang. “Status and Trends in the Education of Racial and Ethnic Groups 2018.” Washington, DC: National Center for Education Statistics, February 2019. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2019038, 156.

78 de Brey, Cristobal, et al., “Status and Trends in the Education of Racial and Ethnic Groups 2018”, 156.

79 “Innovation Measurement: Tracking the State of Innovation in the American Economy.” The Advisory Committee on Measuring Innovation in the 21st Century Economy, U.S. Department of Commerce. January 2008. http://users.nber.org/~sewp/SEWPdigestFeb08/InnovationMeasurement2001_08.pdf

80 Eisenmann, Thomas, Eric Ries, and Sarah Dillard. “Hypothesis-Driven Entrepreneurship: The Lean Startup.” Harvard Business School 9-812–095 (July 10, 2013).

81 “Autonomous vehicles: Boston’s approach.” City of Boston. January 4, 2017. https://www.boston.gov/transportation/autonomous-vehicles-bostons-approach

82 “Reshaping Urban Mobility with Autonomous Vehicles Lessons from the City of Boston”. World Economic Forum, June 2018. http://www3.weforum.org/docs/WEF_Reshaping_Urban_Mobility_with_Autonomous_Vehicles_2018.pdf

83 Wiseman, Janet. “Lessons from Leading CDOs.” Harvard Kennedy School Ash Center for Democratic Governance and Innovation, January 2017. https://datasmart.ash.harvard.edu/news/article/lessons-from-leading-cdos-966

84 Ibid.

85 Ibid.

86 Jacobs, Ronald, and Joshua D. Hawley. Emergence of Workforce Development: Definition, Conceptual Boundaries, and Implications. In R. MacLean and D. Wilson, International Handbook of Technical and Vocational Education and Training, Amsterdam: Springer, 2009.

87 “2018 Workforce Equity Update Report.” City of Seattle, March 2019. https://www.seattle.gov/personnel/resources/pubs/2019_WFE_Update_Report_v5_Final.pdf

88 Blakely, Edward J., and Nancey Green Leigh. Planning Local Economic Development: Theory and Practice 6th edition. Los Angeles: SAGE, 2017.

89 Gramise, Shari O. People and the Competitive Advantage of Place: Building a Workforce for the 21st Century. Cities and Contemporary Society. Armonk, NY: Sharpe, 2006.

90Blakely, Edward J., and Nancey Green Leigh. Planning Local Economic Development: Theory and Practice. 6th edition. Los Angeles: SAGE, 2017.

91 “2018 Workforce Equity Update Report.” City of Seattle, March 2019.

https://www.seattle.gov/personnel/resources/pubs/2019_WFE_Update_Report_v5_Final.pdf

92 Seattle.gov. “Career Development Training Program Map,” January 1, 2019.

https://www.seattle.gov/Documents/Departments/economicDevelopment/workforce/Training%20Program%20Map%2001.18.pd

f. Seattle Public Utilities and Seattle City Light offer entry-level employees a tuition assistance program; Seattle Parks and Recreation and Seattle Public Library offer entry-level employees a mentorship program.

93 Ibid

94 Seattle.gov. “Personnel Rule 3.5 – Out-of-Class Assignments.” Accessed December 19, 2019. https://www.seattle.gov/Documents/Departments/HumanResources/personnel%20rules/Personnel%20Rules%203.5%2019051 0.pdf

95 BCG. “Cultural Change – What Drives Culture Change?” Accessed December 20, 2019. https://www.bcg.com/enus/capabilities/people-organization/seven-crucial-levers-culture-change.aspx

96 Seattle Colleges. “Corporate Training – Specialized Workforce Training.” Accessed December 21, 2019. http://corporatetraining.seattlecolleges.edu

97 Deloitte Insights. “The Automation of Jobs and the Future of Government Work.” Accessed December 21, 2019. https://www2.deloitte.com/us/en/insights/industry/public-sector/job-automation-future-of-work-in-government.html

98 Code for America. “Community Fellowship” Accessed December 21, 2019. https://www.codeforamerica.org/programs/fellowship

99 Academy of Management. “Why Organizational and Community Diversity Matter: Representativeness and the Emergence of Incivility and Organizational Performance.” December 2011. https://journals.aom.org/doi/abs/10.5465/amj.2010.0016

100 City of Seattle. City of Seattle Employer of Choice Initiative Report, June 12, 2019

101 Public Service Division. “White Paper-Salaries for a Capable and Committed Government.” January 10, 2012. https://www.psd.gov.sg/docs/default-source/default-document-library/white-paper salaries-for-a-capable-and-committedgovt.pdf

102 Seattle Jobs Initiative. Employment Pathways and Workforce Diversity Report. 2018.

103 Kimbrough, Carla. “Local Governments Hiring Chief Diversity Officers to Lead Equity and Inclusion Work.” National Civic Review. 2017. http://www.nationalcivicleague.org/wp-content/uploads/2017/11/Kimbrough-2017-National_Civic_Review-1.pdf

104 Ibid.

105 Jobvite. Recruiting Funnel Benchmark Report: Analysis and Actionable Tips to Improve Recruiting Performance Report. May 2017. https://www.jobvite.com/wp-content/uploads/2017/05/Jobvite_2017_Recruiting_Funnel_Benchmark_Report.pdf

106 Seattle Jobs Initiative. Employment Pathways and Workforce Diversity Report. 2018.

107 Berry, Richard. “Taking Talent ABQ’s Skills-Based Hiring to National Stage,” Albuquerque Journal, April 6, 2018, https://www.abqjournal.com/1155261/taking-talent-abqs-skillsbased-hiring-to-national-stage.html

108 Government of Canada. Building a Diverse and Inclusive Public Service: Final Report-Joint Union/Management Task Force on Diversity and Inclusion. December 15, 2017. http://oaresource.library.carleton.ca/wcl/2018/20180119/BT22-184-2017-eng.pdf

109 UK Government Chief Social Researcher’s Office. “Trying it out: The role of Pilots in Policy-Making.” 2003. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/498256/Trying_it_out_the_ro le_of_pilots_in_policy.pdf

110 “The Risks of Outdated Technology: Why Legacy Systems Cost You More Than You Realize,” July 26, 2017. https://www.alvareztg.com/the-risks-of-outdated-technology-why-legacy-systems-cost-you-more-than-you-realize/

111 “Class Specifications.” Accessed January 11, 2020.

https://www.governmentjobs.com/careers/seattle/classspecs

112 “Layoff Guide.” Seattle Department of Human Resources, October 2014. https://www.seattle.gov/personnel/services/pubs/layoff_guide_management.pdf

113 “2018 Salary Schedule Schedule and Compensation Plan.” Seattle Department of Human Resources, 2018. https://www.seattle.gov/personnel/resources/pubs/2018salaryschedule.pdf

114 “2019-20 Seattle Proposed Budget: Glossary.” Seattle City Budget Office, 2019. http://www.seattle.gov/Documents/Departments/FinanceDepartment/19proposedbudget/glossary.pdf

115 Harvard Graduate School of Education. “Pathways to Prosperity: Meeting the Challenge of Preparing Young Americans for the 21st Century.” February 2011.

https://www.gse.harvard.edu/sites/default/files/documents/Pathways_to_Prosperity_Feb2011-1.pdf

116 Ibid.

117 City of Seattle. “Internships.” Employment Opportunities: Career Center. https://www.seattle.gov/personnel/employment/internships.asp

118 Ibid.