The Ethics and Governance of Artificial Intelligence

Curriculum for a course developed by Jonathan Zittrain, Harvard University and Joichi Ito, MIT. Online syllabus shared on the H20 platform. This course will pursue a cross-disciplinary investigation of the implications of emerging technologies, with an emphasis on the development and deployment of Artificial Intelligence. We will cover a variety of issues, including the complex interaction between governance organizations and sovereign states, the proliferation of algorithmic decision making, autonomous systems, machine learning and explanation, 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. The course will entail an intense array of learning and teaching methods. Students will be expected to participate in a variety of activities. May include Media Lab and Berkman Klein Center fellows and affiliates.

Reading List – last updated June 13, 2018

• Opening Event
  • Minds, Brains, and Programs by John R. Searle (Behavioral and Brain Sciences, 1980)
  • “Physiognomy’s New Clothes” by Agüera y Arcas, Mitchell, and Todorov (Medium, 2017)
  • “A Few Useful Things to Know about Machine Learning” by Pedro Domingos (University of Washington)
  • “Machine Learning that Matters” by Kiri L. Wagstaff (JPL)
  • “Thinking Machines: The Search for Artificial Intelligence” by Jacob Roberts (Chemical Heritage Foundation, 2016)
  • “AI Now 2017 Report” by Crawford et al. (2017)
  • “The Ethics of Artificial Intelligence: Mapping the Debate” by Brent Daniel Mittelstadt et al. (Big Data & Society, 2016)
  • “Toward an ethics of algorithms: Convening, observation, probability, and timeliness.” Ananny, M. (Science, Technology, and Human Values, 2016)
  • “The Doomsday Invention” by Raffi Khatchadourian (New Yorker, 2015)
  • [OPTIONAL] “How the machine ‘thinks’: Understanding Opacity in Machine Learning Algorithms” by Jenna Burrell (Big Data and Society, 2016)
  • [OPTIONAL] “Robots as Legal Metaphors” by Ryan Calo (Harvard Journal of Law and Technology, 2016)
  • [OPTIONAL] “Why Zuckerberg and Musk are Fighting About the Robot Future” by Ian Bogost (The Atlantic, 2017)
  • [OPTIONAL] “How I learned to Stop Worrying and Love A.I.” by Robert Burton (The New York Times, September 21, 2015)
• Day 1: Autonomy, System Design, Agency, and Liability
  • Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction” by M.C. Elish (We Robot, 2016)
  • “Machines without Principals (sic): Liability Rules and Artificial Intelligence” by David C. Vladeck (Washington Law Review, 2014)
  • Whose Life Should Your Car Save?” by Azim Shariff et al. (New York Times, 2016)
  • “Machine Learning and the Law: Five Theses” by Thomas Burri (Machine Learning and the Law Conference, 2017)
  • A.I. Versus M.D.” by Siddhartha Mukherjee (New Yorker, 2017)
  • [OPTIONAL] “Executive Summary: The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems” (IEEE, 2016)
  • [OPTIONAL] “When your self-driving car crashes, you could still be the one who gets sued” by Madeleine Claire Elish and Tim Hwang (Quartz, 2015)
• Day 2: Transparency, Explainability, and Bias
  • “The Trouble with Bias” by Kate Crawford (NIPS Keynote, 2017)
  • Weapons Of Math Destruction: How Big Data Increases Inequality And Threatens Democracy (New York: Crown; 2016)
  • “Principles for Accountable Algorithms and a Social Impact Statement for Algorithms” by Nicholas Diakopoulos et al. (Fairness, Accountability, and Transparency in Machine Learning)
  • “Technology is Biased too. How do we Fix it?” By Laura Hudson (Five Thirty Eight, July 20, 2017)
  • “Should Prison Sentences Be Based On Crimes That Haven’t Been Committed Yet?” by Anna Maria Barry-Jester, Ben Casselman and Dana Goldstein
  • State of Wisconsin vs. Eric L. Loomis, Supreme Court of Wisconsin (2016)
  • “How we Analyzed the COMPAS Recidivism Algorithm” by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin (ProPublica, 2016)
  • “A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear” by Avi Feller, Emma Pierson, Sam Corbett-Davies and Sharad Goel (Washington Post, 2016)
  • Databite No. 90 – Kristian Lum: Predictive Policing (Data & Society)
  • “Knowing the Score: New Data, Underwriting, and Marketing in the Consumer Credit Marketplace” (Robinson and Yu, 2014)
  • [OPTIONAL] Stuck in a Pattern: Early evidence on “predictive policing” and civil rights by by David Robinson & Logan Koepke (Upturn, 2017)
  • [OPTIONAL] “Predicting Financial Crime: Augmenting the Predictive Policing Arsenal” by Brian Clifton, Sam Lavigne, & Francis Tseng (The New Inquiry)
  • [OPTIONAL] “Predicting Financial Crime: Augmenting the Predictive Policing Arsenal” by Brian Clifton, Sam Lavigne, & Francis Tseng (The New Inquiry)
  • “Credit Scoring in the Era of Big Data” by Hurley and Adebayo
  • Machines Taught by Photos to Learn a Sexist View of Women by Tom Simonite (Wired Magazine, 2017)
  • “Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine” by Heather Murphy (New York Times, 2017)
• Day 3: Ownership, Control, and Access
  • “The Threat of Algocracy: Reality, Resistance and Accommodation” by John Danaher (Philosophy and Technology, 2016)
  • “Information Operations and Facebook” by Facebook Newsroom (2017)
  • “Million-dollar babies” (The Economist, 2016)
  • “The Race For AI: Google, Baidu, Intel, Apple In A Rush To Grab Artificial Intelligence Startups” (CBInsights, 2017)
  • “The Current State of Machine Intelligence 3.0” by Shivon Zilis and James Cham (O’Rielly, 2017)
  • “Artificial Intelligence Pushes the Anti-Trust Envelope” by Michaela Ross (Bloomberg BNA, 2017)
  • “How Facebook’s Algorithm Suppresses Content Diversity (Modestly) and How the Newsfeed Rules Your Clicks” by Zeynep Tufekci (Medium, 2015)
  • “Facebook Figured Out My Family Secrets, And Won’t Tell Me How” by Kashmir Hill (Gizmodo, 2017)
  • [OPTIONAL] “Big Data: Bringing Competition Policy to the Digital Era – Executive Summary” (OECD, 2017)
  • [OPTIONAL] “Why big tech companies are open-sourcing their AI systems” by Patrick Shafto (The Conversation, 2016)
  • [OPTIONAL] “Data Monopolists Like Google Are Threatening the Economy” by Kira Radinsky (HBR, 2015)
  • [OPTIONAL] “How Baidu Will Win China’s AI Race – and, Maybe, the World’s” by Jessi Hempel (Wired, 2017)
  • [OPTIONAL] “How AI Startups Must Compete with Google” (Startup Grind)
• Day 4: Governance, Regulation, and Accountability
  • “Computer says no: why making AIs fair, accountable and transparent is crucial” by Ian Sample (Guardian, 2017)
  • “Transparent, explainable, and accountable AI for robotics” by Sandra Wachter,Brent Mittelstadt, and Luciano Floridi (Science, 2017)
  • “Our Machines Now Have Knowledge We’ll Never Understand” by David Weinberger (Wired, 2017)
  • “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability” by Mike Ananny and Kate Crawford (New Media and Society, 2016)
  • “Is Effective Regulation of AI Possible? Eight Potential Regulatory Problems” by John Danaher (Philosophical Disquistions 2015).
  • “Algorithmic Transparency for the Smart City” by Robert Brauneis and Ellen P. Goodman (Yale Journal of Law and Technology, 2017)
  • [OPTIONAL] “The Doctor Just Won’t Accept That!” by Zachary C. Lipton (NIPS 2017 Interpretable ML Symposium)
  • [OPTIONAL] “Is Artificial Intelligence Permanently Inscrutable?” by Aaron M. Bornstein (Nautilus, 2016)
• Day 5: Labor, Economics, and Global Trends
  • “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence,” by Jason Furman (expanded remarks from the AI Now expert workshop, 2016)
  • “Regulating the Loop: Ironies of Automation Law” by Meg Leta Ambrose (We Robot, 2014)
  • “Society-in-the-Loop: Programming the Algorithmic Social Contract” by Iyad Rahwan
  • “10 Breakthrough Technologies: Self-Driving Trucks” by David H. Freedman (MIT Technology Review, 2017)
  • “Beijing Wants A.I. to Be Made in China by 2030” by Paul Mozur (New York Times, 2017)
  • [OPTIONAL] “Where machines could replace humans, and where they can’t (yet)” by Michael Chui, James Manyika, and Mehdi Miremadi (McKinsey Quarterly, 2016)
  • [OPTIONAL] “The Relentless Pace of Automation” by David Rotman (MIT Technology Review, 2017)
  • [OPTIONAL] “Basic Income: A Sellout of the American Dream” by David H. Freedman (2016)
  • [OPTIONAL] “End of the Road: Will automation put an end to the American trucker?” by Dominic Rushe (The Guardian, 2017)
  • [OPTIONAL] “China’s Plan to ‘Lead’ in AI: Purpose, Prospects, and Problems” by Graham Webster, Rogier Creemers, Paul Triolo and Elsa Kania (August 1, 2017)
• Day 6 (Conclusion): Ethics, Morals, and Personhood
  • MoreMoral Machine from MIT Media Lab
  • More“Full Tilt: When 100% of Cars are Autonomous” (New York Times, 2017). Read “Picturing the Self-Driving City.”
  • “Resisting Reduction: A Manifesto” by Joi Ito
  • “Everything You Need To Know About Sophia, The World’s First Robot Citizen” by Zara Stone (Forbes, 2017)

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More Information at

• http://blogs.harvard.edu/jzwrites/files/2017/07/1_19_Ethics-and-Governance-of-AI-Syllabus.pdf
• https://www.media.mit.edu/courses/the-ethics-and-governance-of-artificial-intelligence/