The Ethics and Governance of Artificial Intelligence | Griswold505 | January 02, 2018

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The Ethics and Governance of Artificial Intelligence

by Griswold505 Show/Hide
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. EDIT PLAYLIST INFORMATION DELETE PLAYLIST

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  1. 0 Show/Hide More Opening Event
    Original Creator: Griswold505
    2/3-2/4
    1. 0.1 Show/Hide More Minds, Brains, and Programs by John R. Searle (Behavioral and Brain Sciences, 1980)
      Read up to “Open Peer Commentary”
    2. 0.2 “Physiognomy’s New Clothes” by Agüera y Arcas, Mitchell, and Todorov (Medium, 2017)
    3. 0.3 “A Few Useful Things to Know about Machine Learning” by Pedro Domingos (University of Washington)
    4. 0.4 “Machine Learning that Matters” by Kiri L. Wagstaff (JPL)
    5. 0.5 “Thinking Machines: The Search for Artificial Intelligence” by Jacob Roberts (Chemical Heritage Foundation, 2016)
    6. 0.6 “AI Now 2017 Report” by Crawford et al. (2017)
    7. 0.7 “The Ethics of Artificial Intelligence: Mapping the Debate” by Brent Daniel Mittelstadt et al. (Big Data & Society, 2016)
    8. 0.8 "Toward an ethics of algorithms: Convening, observation, probability, and timeliness." Ananny, M. (Science, Technology, and Human Values, 2016)
    9. 0.9 “The Doomsday Invention” by Raffi Khatchadourian (New Yorker, 2015)
    10. 0.10 [OPTIONAL] “How the machine 'thinks': Understanding Opacity in Machine Learning Algorithms” by Jenna Burrell (Big Data and Society, 2016)
    11. 0.11 [OPTIONAL] “Robots as Legal Metaphors” by Ryan Calo (Harvard Journal of Law and Technology, 2016)
    12. 0.12 [OPTIONAL] “Why Zuckerberg and Musk are Fighting About the Robot Future” by Ian Bogost (The Atlantic, 2017)
    13. 0.13 [OPTIONAL] “How I learned to Stop Worrying and Love A.I.” by Robert Burton (The New York Times, September 21, 2015)
  2. 1 Show/Hide More Day 1: Autonomy, System Design, Agency, and Liability
    Original Creator: Griswold505
    2/6
    1. 1.1 “Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction” by M.C. Elish (We Robot, 2016)
    2. 1.2 “Machines without Principals (sic): Liability Rules and Artificial Intelligence” by David C. Vladeck (Washington Law Review, 2014)
    3. 1.3 “Whose Life Should Your Car Save?” by Azim Shariff et al. (New York Times, 2016).
    4. 1.4 “Machine Learning and the Law: Five Theses” by Thomas Burri (Machine Learning and the Law Conference, 2017)
    5. 1.5 “A.I. Versus M.D.” by Siddhartha Mukherjee (New Yorker, 2017)
    6. 1.6 [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)
    7. 1.7 [OPTIONAL] “Executive Summary: The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems” (IEEE, 2016)
  3. 2 Show/Hide More Day 2: Transparency, Explainability, and Bias
    Original Creator: Griswold505
    2/13
    1. 2.1 "The Trouble with Bias" by Kate Crawford (NIPS Keynote, 2017)
    2. 2.2 Show/Hide More Weapons Of Math Destruction: How Big Data Increases Inequality And Threatens Democracy (New York: Crown; 2016)
      Read the Introduction and Chapter 1. Printed copies will be provided.
    3. 2.3 “Principles for Accountable Algorithms and a Social Impact Statement for Algorithms” by Nicholas Diakopoulos et al. (Fairness, Accountability, and Transparency in Machine Learning)
    4. 2.4 “Technology is Biased too. How do we Fix it?” By Laura Hudson (Five Thirty Eight, July 20, 2017)
    5. 2.5 “Should Prison Sentences Be Based On Crimes That Haven’t Been Committed Yet?” by Anna Maria Barry-Jester, Ben Casselman and Dana Goldstein
    6. 2.6 State of Wisconsin vs. Eric L. Loomis, Supreme Court of Wisconsin (2016)
    7. 2.7 “How we Analyzed the COMPAS Recidivism Algorithm” by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin (ProPublica, 2016)
    8. 2.8 "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)
    9. 2.9 "Knowing the Score: New Data, Underwriting, and Marketing in the Consumer Credit Marketplace" (Robinson and Yu, 2014)
    10. 2.10 Show/Hide More Databite No. 90 – Kristian Lum: Predictive Policing (Data & Society)
      Reference “To Predict and Serve?” by Kristian Lum and William Isaac. http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2016.00960.x/full
    11. 2.11 [OPTIONAL] Stuck in a Pattern: Early evidence on "predictive policing" and civil rights by by David Robinson & Logan Koepke (Upturn, 2017)
    12. 2.12 Show/Hide More [OPTIONAL] “Predicting Financial Crime: Augmenting the Predictive Policing Arsenal” by Brian Clifton, Sam Lavigne, & Francis Tseng (The New Inquiry)
      See also https://whitecollar.thenewinquiry.com
    13. 2.13 Show/Hide More [OPTIONAL] “Predicting Financial Crime: Augmenting the Predictive Policing Arsenal” by Brian Clifton, Sam Lavigne, & Francis Tseng (The New Inquiry)
      See also https://whitecollar.thenewinquiry.com
    14. 2.14 “Credit Scoring in the Era of Big Data” by Hurley and Adebayo
    15. 2.15 Machines Taught by Photos to Learn a Sexist View of Women by Tom Simonite (Wired Magazine, 2017)
    16. 2.16 “Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine” by Heather Murphy (New York Times, 2017)
  4. 3 Show/Hide More Day 3: Ownership, Control, and Access
    Original Creator: Griswold505
    2/27
    1. 3.1 “The Threat of Algocracy: Reality, Resistance and Accommodation” by John Danaher (Philosophy and Technology, 2016)
    2. 3.2 “Information Operations and Facebook” by Facebook Newsroom (2017)
    3. 3.3 “Million-dollar babies” (The Economist, 2016)
    4. 3.4 “The Race For AI: Google, Baidu, Intel, Apple In A Rush To Grab Artificial Intelligence Startups” (CBInsights, 2017)
    5. 3.5 “The Current State of Machine Intelligence 3.0” by Shivon Zilis and James Cham (O’Rielly, 2017)
    6. 3.6 “Artificial Intelligence Pushes the Anti-Trust Envelope” by Michaela Ross (Bloomberg BNA, 2017)
    7. 3.7 “How Facebook’s Algorithm Suppresses Content Diversity (Modestly) and How the Newsfeed Rules Your Clicks” by Zeynep Tufekci (Medium, 2015)
    8. 3.8 “Facebook Figured Out My Family Secrets, And Won’t Tell Me How” by Kashmir Hill (Gizmodo, 2017)
    9. 3.9 [OPTIONAL] “Big Data: Bringing Competition Policy to the Digital Era - Executive Summary” (OECD, 2017)
    10. 3.10 [OPTIONAL] “Why big tech companies are open-sourcing their AI systems” by Patrick Shafto (The Conversation, 2016)
    11. 3.11 [OPTIONAL] “Data Monopolists Like Google Are Threatening the Economy” by Kira Radinsky (HBR, 2015)
    12. 3.12 [OPTIONAL] “How Baidu Will Win China’s AI Race - and, Maybe, the World’s” by Jessi Hempel (Wired, 2017)
    13. 3.13 [OPTIONAL] “How AI Startups Must Compete with Google” (Startup Grind)
  5. 4 Show/Hide More Day 4: Governance, Regulation, and Accountability
    Original Creator: Griswold505
    3/20
    1. 4.1 “Computer says no: why making AIs fair, accountable and transparent is crucial” by Ian Sample (Guardian, 2017)
    2. 4.2 "Transparent, explainable, and accountable AI for robotics" by Sandra Wachter,Brent Mittelstadt, and Luciano Floridi (Science, 2017)
    3. 4.3 “Our Machines Now Have Knowledge We’ll Never Understand” by David Weinberger (Wired, 2017)
    4. 4.4 “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)
    5. 4.5 “Is Effective Regulation of AI Possible? Eight Potential Regulatory Problems” by John Danaher (Philosophical Disquistions 2015).
    6. 4.6 “Algorithmic Transparency for the Smart City” by Robert Brauneis and Ellen P. Goodman (Yale Journal of Law and Technology, 2017)
    7. 4.7 [OPTIONAL] “The Doctor Just Won't Accept That!” by Zachary C. Lipton (NIPS 2017 Interpretable ML Symposium)
    8. 4.8 [OPTIONAL] “Is Artificial Intelligence Permanently Inscrutable?” by Aaron M. Bornstein (Nautilus, 2016)
  6. 5 Show/Hide More Day 5: Labor, Economics, and Global Trends
    Original Creator: Griswold505
    4/3
    1. 5.1 “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence,” by Jason Furman (expanded remarks from the AI Now expert workshop, 2016)
    2. 5.2 “Regulating the Loop: Ironies of Automation Law” by Meg Leta Ambrose (We Robot, 2014)
    3. 5.3 “Society-in-the-Loop: Programming the Algorithmic Social Contract” by Iyad Rahwan
    4. 5.4 “10 Breakthrough Technologies: Self-Driving Trucks” by David H. Freedman (MIT Technology Review, 2017)
    5. 5.5 “Beijing Wants A.I. to Be Made in China by 2030” by Paul Mozur (New York Times, 2017)
    6. 5.6 [OPTIONAL] “Where machines could replace humans, and where they can’t (yet)” by Michael Chui, James Manyika, and Mehdi Miremadi (McKinsey Quarterly, 2016)
    7. 5.7 [OPTIONAL] “The Relentless Pace of Automation” by David Rotman (MIT Technology Review, 2017)
    8. 5.8 [OPTIONAL] “Basic Income: A Sellout of the American Dream” by David H. Freedman (2016)
    9. 5.9 [OPTIONAL] “End of the Road: Will automation put an end to the American trucker?” by Dominic Rushe (The Guardian, 2017)
    10. 5.10 [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)
  7. 6 Show/Hide More Day 6 (Conclusion): Ethics, Morals, and Personhood
    Original Creator: Griswold505
    4/10
    1. 6.1 Show/Hide More Moral Machine from MIT Media Lab
      Try a few scenarios.
    2. 6.2 Show/Hide More “Full Tilt: When 100% of Cars are Autonomous” (New York Times, 2017). Read “Picturing the Self-Driving City.”
      Read “Picturing the Self-Driving City”
    3. 6.3 “Resisting Reduction: A Manifesto” by Joi Ito
    4. 6.4 "Everything You Need To Know About Sophia, The World's First Robot Citizen" by Zara Stone (Forbes, 2017)
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June 13, 2018

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Griswold505

Griswold 505

Harvard Law School

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