Incorporating AI Principles in DoD Application Design

INCORPORATING AI PRINCIPLES IN DOD APPLICATION DESIGN

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On July 11, 2018 Bendan McCord Chief Architect of the U.S. Department of Defense Joint AI Center requested that the Defense Innovation Board (DIB) develop a set of principles to guide ethical development and application of AI in DoD. applications. Specifically, DIB was asked to undertake an effort to, “help catalyze a dialogue and set of consultations to establish a set of AI Principles for Defense, particularly while the adoption of this technology is at a nascent stage� (AI Principles Project).

In pursuit of this vision, the DIB held a series of roundtables designed to challenge preconceived mindsets and provide use case driven discussion across a diverse commercial audience.

Harvard University conducted the first set of round tables on January 22, 2019, quickly followed by Carnegie Mellon University on March 13, 2019 and Stanford University on April 26 2019. Finally, on October 31st, 2019, during the start of what would become the Global Pandemic, DIB held a virtual meeting to approve a series of AI principles that established recommended use cases for the ethical use of AI in DoD applications.

These ethical principles, built on established DoD policies and leveraging defense cyber security frameworks, provide a literal map to what AI driven applications could and should look like in the 2021 fiscal cycle. To this end, it behooves technology leaders to understand these principles in relation to application development for DoD to overcome any potential ethical objections, provide relevant use case driven AI enhancements, and ensure a quicker path to procurement.

HOW DOES THE DOD DEFINE AI For DoD, AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior. However the ethical principles established by DIB seem to be narrowly focused on Machine Learning (ML) applications that allows AI frameworks to learn from data without being programmed explicitly.

The Joint Artificial Intelligence Center (JAIC) provides additional insight by providing the four families of Machine Learning (ML) processes that will be loosely governed by the ethical principles complied by DIB:

HOW DOES THE DOD DEFINE AI (CONT):

1.Supervised Learning uses example data that has been labeled by human “supervisors.” Supervised Learning has incredible performance, but getting sufficient labeled data can be difficult, time-consuming, and expensive.

2. Unsupervised Learning uses data but doesn’t require labels for the datas lower performance than Supervised Learning for many applications, but it can also be used to tackle problems where Supervised Learning isn’t viable.

3. Semi-Supervised Learning uses both labeled and unlabeled data and has a mix of the pros and cons of Supervised and Unsupervised learning.

4. Reinforcement Learning has autonomous AI agents that gather their own data and improve based on their trial and error interaction with the environment. It shows a lot of promise in basic research, but so far Reinforcement Learning has been harder to use in the real world. Regardless, technology firms have many noteworthy, real-world success stories.

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