How Might We... Finding Balance in Philosophy, Technology, Ethics and Design
E.Larson — CMU 2018
A Physical Manifestation of Digital Musings
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Index Six Articles Thinking About Design Thinking
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1. Don’t Spill the Drinks: A New Metaphor for Design Thinking
2. The Empathy Boogie
3. But It Smelled So Good: Burning Your Tongue on Freshly Bakes Cookies
E.Louise Larson E.Louise Larson
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5 min read
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E.Louise Larson
5 min read
4 min read
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4. Seeing the Forest for the Trees
5. Designing To Be Self Directed
6. What’s Good? A Co-Design Experiment with AI
E.Louise Larson 7 min read
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E.Louise Larson 4 min read
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E.Louise Larson 8 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
0. How Do We Get There From Here? Presenting a Series on Designing Preferred States The most common definition of Design that you’ll find at the Carnegie Mellon University’s School of Design usually has something to do with preferred situations. This is taken from a former CMU professor and Nobel laureate, Herbert Simon. He believed Design was a process to make the world a better place. This series is exploring the distance between ideas and theories that have shaped Design. Each essay aims has a specific focus as it relates to Design and the Design Thinking Process. In earlier planning stages, I was thinking about this series as five questions for the future of Design. Looking toward the future is at the heart of moving into preferred states, which is why I chose to reposition these articles for new or novice designers.
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The original idea for this series was dedicated to potential futures. By repositioning these articles to be more personal, individuals can reflect on their practice and personal notions of a preferred state. As Design practitioners, we’re all growing and changing with our practice. Each project that comes to close is another ring within our trunk.
Structure and Context The articles presented in this series draw on an array of influences. I’m currently in a master’s program where I’ve had the privilege of exploring a variety of topics from IDEO to AI. This has broadened my scope for these articles. I’m particularly thankful to professors Jonathan Chapman, Molly Steenson, Marti Louw, and John Zimmerman for taking the time to share their expertise with me. Writing these articles has taken me through the fields of Design, Human Computer Interaction, Media Theory, and History. Some of this research illuminated deeper disciplinary interconnectedness. Other research felt meandering and was left on the side of the road while passing through more interesting territory. Ultimately, the diverse mix of ideas came together to form robust models and concepts for Design. Each article was written to be read independently. For curious readers, there is a description of the series at the bottom with a link to the series. The articles were written with the order below in mind: 1. Don’t Spill the Drinks: A New Metaphor for Design Thinking 2. The Empathy Boogie
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3. But It Smelled So Good: Burning Your Tongue on Freshly Bakes Cookies 4. Seeing the Forest for the Trees 5. Designing To Be Self Directed 6. What’s Good? A Co-Design Experiment with AI
Algorithms I am not an expert, but I am interested in using expert methods to reveal something new in the distance between ideas. The last article, What’s Good?, is a research proposal for building an algorithm to autonomously design posters based on Swiss design. Having never built a machine learning algorithm, I had to do a lot of homework. Please forgive any errors in oversimplification.
Learning to Talk Good This project, aimed at aspiring Designers, has made me reflect on the language and communication within Design. Words like “users,” “intervention,” and “immersive” are common terms in practice that Designers easily forget that this coded language excludes novices and the public. To increase inclusion in this conversation, every article uses images and metaphors to make abstract concepts more concrete. All images are my own illustration and original to this project. Lastly, these articles aim to use accessible language. Medium recommends writing for the average adult reading level, which is 6–8th grade in the US. Every article falls within this range on the Flesch-Kincaid Grade Level Analyzer.
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This article is part of a series: How Do We Get There From Here? Each essay explores a component of Design and how it shapes our practices. You can find the full series and related content at How Might We…, ongoing thoughts from my Graduate work at Carnegie Mellon’s School of Design.
More from How Might We...
1. Don’t Spill the Drinks: A New Metaphor for Design Thinking E.Louise Larson
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5 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
1. Don’t Spill the Drinks: A New Metaphor for Design Thinking Exploring the Importance of a Designer’s Process You know the process: Empathize, Define, Ideate, Prototype, Test. Those are the five steps of Design Thinking. You’ve spent hours watching sticky notes cover walls, windows, and every possible surface until a grand idea is brought to life.
Design Thinking can be expansive, divergent, tedious, empathic, and challenging. These images convey a linear process, not a dynamic one. Top Google Image results for “Design Thinking” on Nov 28th, 2018.
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This is the famous process invented by IDEO. It birthed Apple’s first computer mouse and is commonly used to create product mock-ups, business strategies, and even plan joyful lives. If you’re familiar with authors like Richard Buchanan, Donella Meadows, Lucy Kimbell, and Arturo Escobar, you might have your own opinions on Design Thinking. Each of these authors has very different thoughts on the subject, yet they agree on one thing. Design is a process about changing existing situations into preferred ones. This disjoint, however, makes me wonder if the widely accepted purpose of Design is to make situations better, then is Design Thinking the best method to do so? These various applications prove that Design Thinking is dynamic enough to fit a variety of needs. Companies, schools, and designers all adapt Design Thinking to their needs. Manufacturing and other agile workflows favor the double diamond, Engineering has several models like Systematic Variation, and Stanford designers like Ashish Goel conceived of Design Thinking as an infinity symbol.
Design Thinking needs a metaphor. A metaphor makes an abstract concept concrete. It does this by borrowing qualities from a well known object or action and applying those qualities to an abstract concept. Creating a metaphor for Design Thinking is a big task. This metaphor must include the 5-step process, overlap, applied needs, and popular The phases of Design Thinking have been assigned an axis in a coordinate system. On the X axis, Test and Ideate are at either ends of the spectrum. Empathize and Define are along the Y axis.
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understanding. To accomplish this, we’re going to situate Empathize, Ideate, Define, and Test on a single plane. The Prototyping phase has been removed because it can be achieved between rapidly switching between Test and Ideate. This coordinate system for Design Thinking creates unique quadrants that highlight the permeability between each phase. In the examples above, several Design tasks have been mapped to their appropriate coordinate. These tasks might vary with refined prototypes, or as the needs of the project evolve. In the example below, “Brainstorming with Users” is mapped to the Empathy and Ideate quadrant. In these brainstorming sessions, Designers will have to empathize with the users in order to lead the sessions. Users will ideate around the given task. During this ideation, a verbal or low-fidelity prototype might be constructed to demonstrate a point. In this instance, the node travels into the Test and Empathize quadrant, until it moves back into
The node “Brainstorming with users” might move between the Empathy/Ideate and Empathy/Test quadrants.
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brainstorming. It is the responsibility of the Designer to direct the task and node into the most appropriate quadrant. Each phase blends into the other, but empathy is always required when designing for and with other people’s needs.
Balance Designers are the fulcrum on which Design Thinking balances. They take into account their own knowledge and needs, the goals of the project, budget restrictions, time, and overall usability. Designers analyze each stage in the process to work within these constraints to maximize use and good design practice. Balancing all these needs requires Designers to be conversational in empathy, testing, ideation, and definition. This empowers Designers to direct the project flow in each interaction. Each phase blends into the other, but empathy is always required when designing for and with other people’s needs
Each phase of Design Thinking blends into the others. In one affinity mapping exercise, a team might build a paper prototype, ideate a solution, empathize with each other, and test new ideas, all within a single meeting. A more tangible metaphor that demonstrates this balance can be found in for service industry. For anyone who’s waited tables, balancing a tray full of drinks takes patience and skill, and you have to adapt to an ever-changing environment.
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Prototyping has been removed from this model because it can be accomplished by rapidly moving between Test and Ideate.
The glasses on this tray represent the phases of Design Thinking. Each glasses both needs balance and provides balance to the whole tray. In this scenario, the waiter/designer has to use their intuition and carefully trained reflexes. Some glasses are full, others are nearly empty. Each glass requires individual attention while still balancing the whole tray. Empathy is always the biggest glass on the tray. This is because Designers are designing for the needs of other people and situations. In order to do this well, Designers must always practice empathy. I explore the relationship between Empathy and Design in more depth in The Empathy Boogie. This new metaphor aims to make Design Thinking more fluid. As Designers, we are
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constantly moving between each of the steps and always balancing the needs and desires of their users. This process can be exhausting and difficult to explain. But as we grow in our practice and profession, we develop better tools to do so. Waiters develop an intuition for constantly balancing their tray of drinks. As a Designer you’re always balancing empathy, definition, ideation, and testing. Think about your own process. How might you develop a deeper intuition to more meaningfully guide your practice?
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2. The Empathy Boogie E.Louise Larson 5 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
2. The Empathy Boogie How Designers Learn to Understand Themselves In Order To Understand Others Great dancers look as if they’re moving effortlessly. Their synchronized movements look choreographed. They spin and dip with ease. That level of expertise requires practice, timing, and great communication. Dancers also have to be aware of their bodies and environment. For these reasons, dance is like empathy. Empathy is when you can put yourself in someone else’s shoes in order to see how they might be experience the world. We learn empathy through our family, friends, media, and culture. This practice is one that changes throughout our lives and requires constant practice.
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Empathy and Design Empathy is prerequisite for Design Thinking. Design Thinking relies on Designers to engage with the people they’re designing for in order to make something that best meets the needs of the client. At its best, empathy removes the ego and assumptions from Designers and creates space for the participants they’re serving. At its worst, empathy is misunderstood as interrogation. Participant opinions are left unobserved and the design fails to meet their needs. To make empathy work in Design, Designers have to practice empathy. At minimum, this requires three things: a) to be self aware, b) to ask questions, and c) practice self care. Self Awareness
Practicing self-awareness can be uncomfortable. It might mean you realize you’re taller than the average height. Or that you overdressed for an occasion. It might mean that you recognize your power over someone else. Or that you have different privileges from another person. Those details might be uncomfortable sometimes, but they are critical for open lines of communication and empathy. As a Designer, you have to remove your own ego in order understand the client’s needs. Your job is make something for them. In order to understand where your client is coming from, you need to be able to see the world as they do. Ask Questions
Seeing the world from someone else’s shoes requires listening. Literally, hearing. But that’s not all. We must also pay attention to how they’re talking, how they’re dressed, their
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body language, and their attitude. This information provides context to the words that are beings said. These small observations guide every interaction. During an interview or brainstorming session, observations help Designers to ask questions instead of make assumptions. Making assumptions assumes you know the right answer. Asking questions helps you understand what someone else knows. If you’re designing for someone else, its critical to understand what they know. Asking questions can help reveal subconscious idea that clients aren’t even aware of. This establishes trust in the Design team and makes a better overall process. Self Care I rarely go a day without hearing someone describing their behaviors as self care. Sometimes this is a thinly disguised justification for their actions. Other times, self care is a careful attempt at self preservation. True self care is about finding balance in your life to rest, grow, and meaningfully live. In Design practice, this self care is necessary to recover from empathy. Empathy is work. You peer into other people’s lives. It is exhausting to see the world from such radically different points of view. Sometimes these lives can appear more successful or adventurous than our own. This mental trap is hard to break free
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from. If you find yourself comparing instead of empathizing, how might you feel empathy for yourself in this situation?
A Model Relationship Self care isn’t something taught in school, but is is critical to your practice. Empathy is the mechanism by which we refine our own understandings of the world, and make space for other understandings to exist. Designers have the responsibility to steward empathy in their projects and teams to best represent user needs. Consider your own life. Who is the person you call when you’re having a bad day? That someone is empathizing with you? How does it make you feel knowing that they can see where you’re coming from? That feeling of trust is the model of what an ideal relationship between stakeholders and designers should be. How can you bring that into your own projects?
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3. But It Smelled So Good: Burning Your Tongue on Freshly Bakes Cookies E.Louise Larson 4 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
3. But It Smelled So Good: Burning Your Tongue on Freshly Bakes Cookies Exploring Bodies, Memories, Media, and the Military Embodied Cognition The smell of cookies baking can cause a lot of thoughts to run through your mind. If your housemate loves to bake, you might remember the last batch of cookies they made. If you already had cookies at lunch, you might be trying to justify eating fresh-baked cookies. Let’s not forget this is your kitchen. Maybe you feel comforted by familiar smell of your housemate’s baking. Maybe you remember a really good batch of cookies, warm with melted chocolate. Or, if you were too eager for sweets, you might remember burning your tongue on a cookie that was too hot. All of these thoughts, memories, smells, and experiences are part of your cognitive process. Writing this makes me think of the cookies my grandma made, a memory unique to me.
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These personal memories and experience are also related to our bodies. We remember smell, taste, texture, sound, and sight. This body-memory is called embodied cognition. Embodied cognition is at the crossroads of science and philosophy (not unlike Design). It’s a way of thinking about the mind that takes into account your unique language, senses, memory, brain chemistry, abilities, and experience. People who study embodied cognition believe that a person’s body is an instrument to help make sense of the world.
Media Determine Our Situation How do you see the world? Do you notice when a movie is in High Definition? Does your phone automatically adjust it’s brightness when you go into a dark room? These are examples of the relationship between media and our senses. Media and senses have been reciprocal for hundreds of years. The first well known example is linear perspective. Most students in the US education system learn to draw in linear perspective around 6th grade. This day in art class is the result of early Renaissance artists working to translate the 3D world onto a 2D surface. The transformation of art, architecture, visual media, and design was forever altered at this invention. Humans had finally taught themselves to see the world in a mathematically accurate way. This same transformation of human sense perception can be seen today in the invention of Apple’s responsive Retina Display, featuring “True Tone technology…[that]
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automatically adjusts to match the color temperature of the light around you — for a more natural viewing experience.” Media expands our ability to understand human senses. How do you think the new forms of media, like augmented reality, might affect our senses and embodied cognition?
Models, Metaphors, Militaries “We knew nothing about our senses until media provided models and metaphors.” Friedrich Kittler said that in 1999 as part of his Optical Media lecture series. Kittler was a German media theorist who‘s work explores media, technology, and the military. One of his most interesting ideas is that “media” is made up of many smaller inventions. These smaller inventions were, however, always created to meet military demands. Media is an assembly of military technology repackaged and adopted domestically.
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“Technical innovations — following the model of military escalations — only refer and answer to each other…which is an overwhelming impact on sense and organs in general.” These military inventions eventually became radio, television, the internet, and virtual reality. Kittler was concerned with how we would come to understand our senses if the media we use was created with military intent. This concern extends into empathy. Empathy is something we learn by being in the world and examining our own body-memory. How does this relationship between empathy, media, and our senses change if we examine this connection to the military?
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4. Seeing the Forest for the Trees E.Louise Larson 7 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
4. Seeing the Forest for the Trees How Senses and Reason Frame Our World Senses, worldview, and probability are necessary for reason. This makes reason a tricky thing. The most common definition of reason is your ability to make sense of things, even when something is new. Immanuel Kant was a German philosopher in the 18th century. He is often pointed to as the figure that moved philosophy forward. Before Kant, philosophers were divided into two main camps. The first believed that all knowledge was learned through the senses. The second believed that humans are reason their way into knowledge. Variations of these beliefs are still found in philosophy today. Most people actually believe both, but apply them differently. The field of Design is no exception.
Making Sense of Senses There are five human senses: sight, smell, taste, touch, hearing. These senses give us the tools we need to interpret the world. Our brains collect the data being received through
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our senses, analyze it, and use that information to make decisions. Sometimes this information is also stored in our brains, creating memories. I’ve written about our understanding of sense perception here. In essence, our senses determine how we know how to see the world. Knowing how to see the world is how we create our own personal worldview. This worldview is a filter that we use to reason. Senses, worldview, and reason give meaning to our actions. For Designers, this meaning becomes our process and aesthetic. Through the practice of empathy, Designers can know the boundaries of their worldview and ego in order to set them aside. This process creates space for clients to share their worldview for the design process. The difference in individual experiences as mediated by our senses is called embodied cognition. It is a body-memory that is unique for everyone, and it is two-fold for Designers. All Designers bring their own embodied cognition to their projects, and they must also design for the embodied cognition of others.
Reason in Design Designers empathize with their clients and stakeholders. Designers can listen and observe what the users might be feeling, but they cannot know. Its impossible to walk around in the bodies and memories of other people. This is where Reason and Design meet. The relationship between Design and Reason go back to ancient Greek arguments about “craft” versus “art.” Though this discourse is still relevant today, the field of Design has carved out a niche for itself in this debate. The primary methods of reason in Design are Inductive, Deductive and Abductive.
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Inductive Reason Q: What do you see in a forest? A: Trees. This generalized answer is the result of inductive reason. Inductive reasoning is the process of making observations about the world and making a generalized conclusion. Simply saying “Tress” doesn’t account for the specific species of trees, but it gets the point across. Deductive Reason Q: Who lives in a forest? A: Deer? Yes. Squirrels? Yes. Aliens? No. Only Deer and Squirrels live in the forest. This Deductive reasoning uses the process of elimination to get to a conclusion. This conclusions, however, can may not encompass all the details. Do rabbits also live in the forest? Abductive Reason Q: What is a forest? A: A forest has trees, squirrels, deer, and sometimes rabbits. Other animals and plants also live in the forest. This statement uses abductive reason to make an educated guess about what a forest might be. Design researchers and philosophers describe the process of Design Thinking as Abductive Reasoning. It is the unique combination of generalizing existing ideas while
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also narrowing down existing choices in order to generate new ideas. These new ideas are the best guess for a design that will meet the needs of stakeholders. Kees Dorst summarized the history of reason and design a paper presented that the “DTRS8 Interpreting Design Thinking: Design Thinking Research Symposium” conference in 2010. He described Abductive Reason in Design as the result (Value) of what you are designing (What) plus your method and constraints (How). This idea is based on Charles Sansers Peirce’s work on formal logic as interpreted by Dutch Industrial Designer Norbert Roozenburg.
The mental math of abductive reason in Design.
Dorst breaks Abduction into two forms. The first form is most similar to how we think of problem solving. What we will make is unknown. We only know how we’re going to problem solve and the goal value.
Abduction-1 = the “What” is missing in this design equation
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The second form of Abduction is more complicated. In this version we only know the goal value. The “What” and “How” are both missing.
Abduction-2 = “What” and “How” are both missing
To make sense of this second form, Dorst recommends we invent a flexible scenario by using induction.
Abduction-2 = Induction solves for “How”
Then, we can use Abduction-1 to make up what the “What” might be.
Abduction-2 = Use Abduction-1 to solve for the “What”
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This mental math is one of the unique tools of Design. Designers can use empathy, personal knowledge, reason, and Design Thinking to make educated guesses toward solutions.
Frame, Frame, and Frame Again
Designers use their empathy, senses, and worldview to shape their work. They also take into account the user’s point of view and preferred state. Both the Designer and user exist within a world of infinite possibility. In order to find a potential path forward, Designers use abduction to guide their process. This happens rapidly and can radically influence the outcome of a project. This process of creating potential paths forward can only happen after a few assumptions have been solved for, like the What, How, and Value. In order to solve for these three things, Designers might have to go through several iterations of abduction. This is called “framing.” Dorst describes framing as the “reasoning in design situations”.
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Frames are made from the bits of stuff gathered from the Designers, Users, project demands, and external world.
Frame 1 was created by borrowing bits from Designers, Users, and the external world.
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Frames are lenses created in order to look more closely at a problem. Each lens provides a slightly different way of looking at what the problem might be. Frames might be created for defining users needs, critiquing a prototype, or ideating new solutions. Throughout a single project, Designers create many frames. Each frame is unique and creates different potential solutions. No design can meet every single need in Frames 1, 2, and 3.
Framing and re-framing design problems create many different vantage points from viewing problems and sleuthing out potential solutions.
Even if a design can’t meed every need in every frame, it is the responsibility of Designers to accurately frame user needs. As designers begin to better understand the users needs, this is new information for a more nuanced frame. Users will also begin to understand their own needs differently as the project progresses. This might sound terrifying because you will need to create more frames to meet these new needs, but this is a sign of learning and progress. As users learn how to articulate their needs better, they become more valuable resources. These users also serve as a model for how to teach new users to understand the thing you’ve designed. Think about how you learned to use a complex piece of software. Did you take a class? Look up tutorials? These are all examples of how you used reason to solve a problem and increase your personal autonomy.
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Autonomy is a Design principle with the goal of empowering beginners and emboldening experts. Have you ever created a plan to coach a user to learn something new so they could do it by themselves? That design challenge creates user autonomy through the perfect balance of empathy, reason, and framing.
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5. Designing To Be Self Directed E.Louise Larson 4 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
5. Designing To Be Self Directed Exploring the Ambiguity of Autonomy in Design Autonomy is the amount of freedom a person can act within. In Design, the principle of autonomy aims to give users enough tools and information to accomplish their goals. But not so much information they feel overwhelmed and give up. Have you ever purchased a new app and were forced to click through a tutorial of features? That app was trying to increase your autonomy in the long run by restricting it in the short term. By forcing you into a tutorial, the app was training you on new or advanced features that will come in handy as you gain expertise. This is common practice in apps, games, and software. Autonomy is also experienced in the physical world. For example, putting training wheels on a bicycle gives you the autonomy to ride by yourself. Getting a driver’s license gives you the autonomy to travel long distances on your own schedule. These different measures of autonomy, however, all exist within larger systems. What can we learn about the relationship between Design and autonomy in today’s politics, society, and industry?
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Designing the Perfect Park Designers work across all public and private sectors. They use Design Thinking for executive teams, policymaking, and governments. Their unique skills navigate big problems to find meaningful solutions. Designers might be hired by a city government to help design a public park. For this kind of public works project, a design team would begin by collecting data from the public. There would be a lot of interviews and sticky notes to capture how people feel about parks. Then the designers would sort this data into meaningful chunks. They would look for relationships between these larger chunks of data. Once the designers have analyzed the data, they would brainstorm a new park. They would make sketches of the ideal park, then meet with the city government for feedback. This begins a series of sessions between the designers and the public, redesign, then designers and the government, redesign, then back to the public‌you get the idea. This process continues until all needs are met, budget constraints are met, or time demands are met. Then the construction of a new public park begins.
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In this public parks example Designers spend a lot of time going back and forth between different groups of people. This back-and-forth is one thing that makes Design so valuable. Designers can listen to very different people share very different concerns and needs. They use this information to make something custom for the stakeholders they’re working with. It is very unlikely, however, that every single need will be met. Designers have to make choices that prioritize some needs over others. This is where the principle of autonomy is applied.
But For Whom? Designing for autonomy means making decisions with people in mind. The people for whom you’re designing should have enough freedom and information to make their own best decision. Let’s go back to the public parks example. This park has two kinds of trails. The first kind of trail can be used for running, walking, biking, and rollerblading. No gas-powered motorized vehicles are allowed. The second set of trails are only for motor-sports. Both trails are clearly marked with directional signage and publicly available maps. Any citizen concerned with their daily jog can choose a safe route, distance, and time of day they want to run. Citizens with dirt bikes can choose trails that won’t conflict with joggers. Both citizen’s have enough information and autonomy to use the park how they best see fit. This is great design practice.
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This parks example works great for autonomy in common physical systems. It becomes much harder when applied to abstract, uncommon, or new systems.
Death By Design Phones and laptops create a lot of autonomy for a person. You can work at home. Stay connected with friends while traveling. Customize software and apps to your liking. These products, and the whole Tech industry, always has to design for autonomy in abstract, uncommon, or new systems. This leads to amazing innovations, and some unique business practices. Several Tech companies have recently been scrutinized for shortening the lifespans of their products, forcing customers to buy replacements faster. This is called “planned obsolescence.” It happens when a company uses materials or software designed to fail after a certain period of time. The logic is simple. If your phone or laptop fails, you will just buy a new one. In fact, you will probably buy the newest version of the exact same thing you lost. The forced buying cycle of planned obsolescence is a business decision that decreases personal autonomy.
Difficulty in Design Given the examples of city parks and cell phones, the principle of autonomy might feel ambiguous. That’s because it is. Designing for autonomy is really difficult. In Don’t Spill the Drinks I proposed a new metaphor for Design Thinking. The idea is that Design Thinking should feel more like a balancing act than an elevator. This balance
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is meant for Designers to guide the design process through the murky waters of design thinking without losing sight of how each phase is connected. This balance requires Designers to practice empathy and reason in equal measure. As you grow in your expertise and process, this balance becomes more intuitive. Though this article ends in ambiguity, I propose a co-design experiment with Artificial Intelligence to explore how Design without Design Thinking might change a process.
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6. What’s Good? A Co-Design Experiment with AI E.Louise Larson
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8 min read
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E.Louise Larson Easily excitable. Carnegie Mellon School of Design. IDeATe adjunct. CEO and co-Founder @ Prototype PGH.
Dec 5 • 3 min read
6. What’s Good? A Co-Design Experiment with AI Building a Spectacle to Explore Human Feeling and Good Design Abstract This project proposes to explore the relationship between images, design, and autonomy through co-design. A co-designer will be built using a Bayesian Generational Adversarial Network (GAN). This algorithm will autonomously design advertising posters so we might observe what autonomously generated posters reveals about human aesthetics and the design process.
Introduction Art and Spectacle The idea of autonomously generated art is inspired by Situationist thinking and Avant-garde art. Not only were these movements related, but they both explored the relationship between identity and image.
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Situationists explored the disconnect between hyper-capitalism and social alienation. They adopted Karl Marx’s theory of alienation and believed that people felt social alienation when they gave up living authentic lives in order to do more work. Situationists were concerned that the more people wanted to buy, the less they wanted to live. Avant-garde art is a broader movement than Situationism: but these artists were concerned with living authentic lives. A consistent theme in the Avant-garde is questioning the relationship between consumer and artist. Illuminating this power dynamic called into question consumer desires and social roles. These exploratory thinkers wanted people to contemplate how their identities were largely designed by the society they were born into. As a designer, I’m also interested in this dynamic. Specifically, I’m interested in the intentions and ethical implications of designed objects, experiences, and environments? Who are these things for? What is the relationship between these things and the larger contexts they exist within? Science and Anathema Computer Scientists use simple math, like probability, to write complex algorithms for Artificial Intelligence (AI). The AI uses these algorithms to make decisions and follow steps to accomplish a task. For a human, this would be like using a coin flip to make every decision. On its own, AI doesn’t do a very good job of learning. There is new research, however, exploring how robots can mimic embodied cognition and procedural memory. Researchers from MIT have been working on a robot named Anathema that autonomously learns. Learning, however, isn’t like following a recipe. It is dependent
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on the context and content. For this reason, learning is something that science is still researching. Even though learning can’t be prescribed with 100% accuracy, there are some general methods that can commonly be applied. Lectures, experiments, instructions, and practice are all variations on how people learn. Each of these kinds of learning have different kinds of associated reasoning. The most common is deductive reasoning. This kind of learning can be imitated. To mimic human learning using deduction, these researchers taught Anathema to plan. Planning is a process by which you use your senses to observe your current situation, frame the problem space, ideate potential solutions, and test what is possible. Sound familiar? Design Thinking is a kind of planning. If this robot can learn to plan, can it learn Design Thinking? Robo-planning MIT’s planning research is a big deal for AI and robots. The paper they recently published is here. To explain how the robot works they use the example of a robot butler. Let’s imagine that the family who own’s this butler moves into a new house. In this new house the robot has to learn a new floor plan in order to perform its tasks. If you’ve ever moved into a new home with a pet, you have probably seen a pet adapt to their new environment in a similar way. A pet dog might run around the house sniffing each room until he finds a spot that meets his dog-requirements for a nap. Your robot butler doesn’t have a sniff sensor, so instead it uses vision sensors for data input. This “seeing” is like taking a 3D picture of a room. The
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robot uses this 3D snapshot to navigate then processes how it might perform its task in this environment. This is how robot learning, or planning, happens. If the robot is tasked to clean a glass, what does that planning process look like? In this scenario, the robot butler is in a new home. It doesn’t have a mental map of where the kitchen is. The robot first needs to interpret the data around itself to determine if it is already in the kitchen. If the glass has liquid in it, the robot needs to pour the liquid into the kitchen sink then place it in the dishwasher. What if the dishwasher door is closed? How does the robot need to adjust its patterns of behavior in a constantly changing environment? Once the robot has identified the kitchen and dishwasher, it will follow a process like this:
What human thinks of as a 2-step process takes a robot almost 10 steps.
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This dishwasher example seems simple at first, but ends up being really complicated. The robot has a lot of data to calculate in order to perform simple tasks. But, the research team working on this robot, came up with a really clever system of reason for the robot. When this robot has identified a kitchen, it also identifies all the data it is programmed to associate with kitchens. It assumes that a sink, oven, refrigerator, and dishwasher are all in the kitchen. If the robot is in a kitchen, there must be a sink. If the robot has a dirty glass, it must have some liquid that needs to be poured out. All liquid must be poured into a sink. All kitchens have a sink. All dirty glasses must be taken into the kitchen, then poured into the sink. These assumptions drastically shortens the butler’s process list.
In this second scenario, the butler is programmed to better plan how it will load the glass into the dishwasher. Many of the yes or no questions that complicate the first scenario are compressed in the second scenario. There is a big difference in this kind of thinking. The robot’s raw sensory data, like the 3D snapshot, is low-level input. The robot must
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analyze this input to determine what might be useful for the task at hand. Analyzing these complex situations to make process judgments is high-level planning. This high-level planning is similar to how humans reason. Robo-reason As discussed here, human reason is fundamental to Design Thinking. Reason allows humans to use their senses, intuition, and best judgment to frame a design problem. This process requires balancing empathy and definition with testing and ideation. This high-level planning is choosing which bits of information the robot might need in order to accomplish a given task. This framing helps the robot make better decisions. If robot butlers and complex AIs are learning to conduct high-level planning, how might they also learn abductive reason? If the robot can process millions of data points to make millions of frames in just a few seconds, wouldn’t these robots make excellent designers? This line of inquiry involves many interdisciplinary fields. The least of which is Design.
Research Hypothesis A robot like MIT’s Anathema creates many questions at the intersection of technology, design, and ethics. This proposal outlines how to explore a subset of those questions using a Bayesian Generational Adversarial Network (GAN). This Machine Learning (ML) algorithm will use the Carnegie Mellon Swiss Poster Collection and a set of design rules in order to autonomously generate new Swiss posters.
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There are primary questions I’m asking in this experiment: 1. What’s Good? What might humans interpret in these artificial simulations of Swiss posters? What might human interpretations of GAN-generated posters reveal about aesthetics? 2. What does a Swiss advertising language look like? These posters use the national languages of Switzerland (German, French, and Italian). Presumably, the AI will generate a language of its own derived from the Swiss training data. Will human observers be able to tell what was GAN-generated? 3. How might AI use data derived from Design (and therefore human sense perception, empathy, reason) to create “good” design? How might Design (and art) change the way an AI is trained to perceive the world and plan? Swiss Posters Swiss posters are a hallmark of 20th century design that continue to shape visual communication to this very day. Carnegie Mellon University has a collection of over 300 posters donated by Ruedi Ruegg.
http://luna.library.cmu.edu/luna/servlet/view/all?sort=date%2Cdesigner%2Cclient%2Ctitle
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The CMU collection covers the 1950s to present day. These posters are known for a clear and expressive style that specifically leverages grid, color, form, and typography. Posters were created for a variety of advertising purposes from business communications to museum exhibit announcements. German, French and Italian are all frequently used in the poster design.
Materials and Methods Generational Adversarial Networks are unique algorithms that make and refine images. A researcher gathers really good examples of the kind of images the GAN should make and puts these images in a training data set. The GAN uses this data set like an answer key. The GAN uses random noise to generate random images. These images are fed into the discriminator. The discriminator decides what images are true to the answer key. Images that don’t look like the training set are marked “false” and discarded. Images that look more like the training data are “true” and act as positive reinforcement. The ideal output for a GAN is an image that accurately resembles the training data. That whole process looks like this:
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This project will use pyTorch to build the GAN. The training set would be made of Swiss Posters scraped from Carnegie Mellon University’s special collection. The Bayesian analysis would rank grid, color, form, and typography in the Swiss poster training data. The addition of these data points make for a more complex GAN. Each GAN-generated has to be marked “true” for grid, then color, then form, then typography. The below image demonstrates how the GAN uses “true” training data to generate gridaligned Swiss posters. “True” grid posters would then have to pass color in order to continue the positive reinforcement cycle.
Conclusion This experiment is rooted in science but is meant to explore art, design, and psychology. What’s Good is a proposal to build a generational algorithm to create art. This project uses recent breakthroughs in artificial intelligence and computer science to explore how people might feel about autonomously generated design.
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The below examples are the top Google Image results for “Bad Graphic Design.� These images may not be the most beautiful, but they were clearly designed with a specific intention. Will humans value these designs more because they were created by another human? Presumably neither these posters nor the Swiss posters were made using the Design Thinking process as we know it today. Design Thinking or not, humans use their senses, empathy, and reason to negotiate problem spaces. Can AI be trained to analyze problems in the same way?
Bibliography From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning. http://lis.csail.mit.edu/pubs/konidaris-jair18.pdf
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