Ethics and Governance of Artificial Intelligence (Fall 2020)

Jonathan Zittrain (HLS)

This reading group will pursue a cross-disciplinary investigation of the opaque complex adaptive systems that are increasingly in public and private use. We will explore the proliferation of algorithmic decision making, autonomous systems, and specifically machine learning; the search for balance between regulation and innovation; and the effects of AI on the dissemination of information, along with questions related to individual rights, discrimination, and architectures of control.

6 Intellectual Debt 6 Intellectual Debt

6.1 [Pre-class activity] Please come to class having identified an example of a use of machine learning (current or prospective) that you find to be deeply unsettling, as well as an example of a use you find to be exciting. 6.1 [Pre-class activity] Please come to class having identified an example of a use of machine learning (current or prospective) that you find to be deeply unsettling, as well as an example of a use you find to be exciting.

TBD

6.2. “The Bitter Lesson” by Rich Sutton (2019)

“The Bitter Lesson” by Rich Sutton (2019)

6.3. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead” by Cynthia Rudin, (Nature, 2019)

“Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead” by Cynthia Rudin, (Nature, 2019)

6.4. “The Hidden Costs of Automated Thinking” by Jonathan Zittrain (The New Yorker, 2019)

“The Hidden Costs of Automated Thinking” by Jonathan Zittrain (The New Yorker, 2019)

6.5. ”Artificial Intelligence’s ‘Black Box’ Is Nothing to Fear” by Vijay Pande, (The New York Times, 2018)

”Artificial Intelligence’s ‘Black Box’ Is Nothing to Fear” by Vijay Pande, (The New York Times, 2018)