- Use of predictive algorithms in public vs. private settings, with emphasis on the criminal justice system
- Comparative approaches to algorithmic transparency and accountability (e.g., auditing, technological due process)
- Trade-offs between predictive accuracy and competing values (e.g., fairness, transparency, explainability)
- Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, introduction and Chapter 1 (Crown Publishing Group, 2017)
- Stanford Case Study: Algorithmic Decision-Making and Accountability
- “A Guide to Solving Social Problems with Machine Learning" by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan (Harvard Business Review, 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)
- “Algorithms, Correcting Biases” by Cass Sunstein (Social Research, 2019)
- Video Explainer on Rawls’ Original Position (Wireless Philosophy, 2014)
- “Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights,” pp. 1-18, 22-24 (White House, May 2016)
- Handout on “Introduction to Probability and Machine Learning” for Background
- “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning” by Sam Corbett-Davies, Sharad Goel (ArXiv, 2018)
- “Machine Bias” by Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner (ProPublica, 2016)
- “How we Analyzed the COMPAS Recidivism Algorithm” by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin (ProPublica, 2016)
- “Can you make AI fairer than a judge? Play our courtroom algorithm game” by Karen Hao and Jonathan Stray (MIT Technology Review, 2019)
- “21 Fairness Definitions and Their Politics” by Arvind Narayanan (Tutorial at Conference on Fairness, Accountability, and Transparency, 2018)
- “Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges” by Songul Tolan (ArXiv, 2019)
- "Algorithmic decision making and the cost of fairness” by Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, Aziz Huq (Proceedings of KDD'17, 2017)
- “Inherent Trade-Offs in the Fair Determination of Risk Scores” by Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, sections 1 and 5 (Proceedings of Innovations in Theoretical Computer Science, 2017)
- “A Guide to Solving Social Problems with Machine Learning" by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan (Harvard Business Review, 2016)
- “If You Give a Judge a Risk Score: Evidence from Kentucky Bail Decisions” by Alex Albright
- Alan Gerber and Donald Green, Field Experiments, Chapter 1
- “Human Decisions and Machine Predictions” by Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan (The Quarterly Journal of Economics, 2018)
- "Improving Refugee Integration through Data-Driven Algorithmic Assignment" by Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner, Duncan Lawrence, Jeremy Weinstein (Science, 2018)
- “From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation” by Scott E. Carrell, Bruce I. Sacerdote, and James E. West (Econometrica, 2013)
- “Randomized Controlled Field Trials of Predictive Policing” by Mohler et al, (Journal of the American Statistical Association, 2015)
- Frank Pasquale, The Black Box Society (Harvard University Press, 2016)
- “Discrimination in the Age of Algorithms” by Kleinberg et al, pp. 1-6 (NBER, 2019)
- “Algorithmic Impact Assessments: Toward Accountable Automation in Public Agencies” by Dillon Reisman, Jason Schultz, Kate Crawford, Meredith Whittaker, pp. 7-20 (AI Now Institute, 2018)
- “Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?” by Paul Laat (Philosophy & Technology, 2018)
- “The Scored Society: Due Process for Automated Predictions” by Danielle Citron and Frank Pasquale, pp. 18-33 (Washington Law Review, 2014)
- “Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms” by Nicol Lee, Paul Resnick, and Genie Barton (Brookings Institution, 2019)
- “When an Algorithm Helps Send You to Prison" by Ellora Israni (New York Times, 2017)