DS457/DS657/JD673: Law and Algorithms
This cross-cutting and interdisciplinary course, taught jointly between the School of Law, the faculty of Computing and Data Sciences, and Computer Science investigates the role that algorithms and automated decision-making systems play in law and society. The course connects legal and computational concepts of transparency, fairness, bias, trust, and privacy, though a series of case studies that present recent applications of technology to legal and regulatory situations and explore the challenges in regulating algorithms.
Legal concepts explored will include evidence and expert witnesses, anti-discrimination law concepts of disparate impact and disparate treatment, sectoral information privacy regimes, and public access and transparency laws. Computational concepts explored will include artificial intelligence and machine learning, secure multi-party computation, differential privacy, and zero-knowledge proofs.
This course will meet in LAW 204 in the Law Tower. The syllabus for this course can be found as a PDF here.
Class 1: Intro to Law and Algorithms (Jan. 20)
Intro to Computational Thinking:
- Kristian Lum & Rumman Chowdhury, What is an “Algorithm?” It Depends Whom You Ask, MIT Tech. Review (Feb. 26, 2021) – read all. (A copy of this is also available from the instructors if you cannot access this website.)
- Gerd Gigerenzer, Ralph Hertwig, Eva van den Broek, Barbara Fasolo, and Konstantinos V. Katsikopoulos, “A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts?, 25 Risk Analysis 623 (2005) — read parts 1, 2, 5, and 6.
- Rebecca Kelly Slaughter, Algorithms and Economic Justice, UCLA Law (2020) – read Section II only
Intro to Law and Legal Thinking
- Andrew Sellars, A Practical Introduction to United States Law for Technologists – Law students can skim, as it will be largely familiar to you already. CS students read all.
Law, Algorithms, and Power:
- Ari Ezra Waldman, Power, Process, and Automated Decision-Making, 88 Fordham L. Rev. 613 (2019) – read Section I only
- Catherine D’Ignazio and Lauren F. Klein, “The Power Chapter,” from Data Feminism (2020) – read sections on “Data Science by Whom?” and “Data Science for Whom?”
Influences and Collisions Across Domains:
- Langdon Winner, Do Artifacts Have Politics?, 109 Daedalus 121 (1980) – read “Technical Arrangements and Forms of Order,” pp. 123–28.
- Joel Reidenberg, Lex Informatica: The Formulation of Policy Rules through Technology, 76 Tex. L. Rev. 553 (1997) – read section III only, pp. 568–76.
- Edward K. Cheng, Fighting Legal Innumeracy, 17 Green Bag 2d 271 (2014) – read section II only.
The social effect of algorithms:
- Ian Bogost, The Cathedral of Computation, The Atlantic (Jan. 15, 2015)
- Alan R. Wagner, Jason Borenstein, Ayanna Howard, Overtrust in the Robotic Age, Comm. of the ACM (Sept. 2018) "Overtrust in the Robotic Age"
- Madeline Clare Elish, Don’t Call AI “Magic”, Data & Society (Jan. 17, 2018)
The regulation of algorithms:
- Danielle Keats Citron, Technological Due Process, 85 Wash. U.L. Rev. 1249 (2008)
- Tal Zarsky, The Trouble With Algorithmic Decisions, 41 Science, Technology, & Human Values 118 (2016)
- Olivier Sylvain, Regulating AI: The Question Now is No Longer Whether, But How, Balkinization (Dec. 22, 2020)
- Plus many, many more readings to come in subsequent classes!
More introductory law & algorithm readings:
- David Aurebach, The Stupidity of Computers, n+1 (2012)
- Lawrence Solum, Legal Theory Lexicon: The Common Law, Legal Theory Blog (Jan. 31, 2021)
- Daniel Kunin, Jingru Guo, Tyler Dae Devlin, and Daniel Xiang, Seeing Theory: A Visual Introduction to Probability and Statistics
- Rob Kitchin, “Conceptualizing Data,” from The Data Revolution (2014).
Algorithms and power:
- Ruha Benjamin, 2020 Vision: Reimagining the Default Settings of Technology & Society (2020)