Reading List and Course Outline

Below is a schedule of class topics and readings. They are subject to change by the instructors, and all changes will be communicated.

1: Codes of Ethics, Disciplinary Perspectives, and Case Studies

●      Existing Codes of Ethics: ACM Code of Ethics and Professional Conduct, Artificial Intelligence at Google – Our Principles, Ethical OS Risk Mitigation Checklist

●      Individual profiles: Ellora Israni, John Luttig, Henry Tsai, Chris Cox

●      “Feynman’s Error: On Ethical Thinking and Drifting” by Dan Munro (Dan’s blog, November 2018)

●      “Optimize What?" by Jimmy Wu

Supplementary:

●      “Two Cultures” by C. P. Snow (The Rede Lecture, 1959)

●      “Solving for Pattern” by Wendell Berry (Chapter 9 in The Gift of Good Land: Further Essays Cultural & Agricultural, North Point Press, 1981)

●      Partnership on AI Tenets

●      “Of Course Congress Is Clueless About Tech—It Killed Its Tutor” (WIRED, 2016)

●      “Data Science as Political Action: Grounding Data Science in a Politics of Justice” by Ben Green (2019)

Algorithmic Decision-Making | Promise & Perils

●      Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Introduction and Chapter 1 (Crown Publishing Group, 2016)

●      John Rawls, A Theory of Justice, pp. 10-24, Section 3 “The Main Idea of the Theory of Justice,” Section 4 “The Original Position and Justification,” and Section 5 “Classical Utilitarianism,” (Harvard University Press, 1971; revised 1999)

Supplementary:

●      “Algorithms, Correcting Biases” by Cass Sunstein (Social Research, 2019)