Fall 2019: Law for Algorithms

A collaboration between Boston University and UC Berkeley for CS and law graduate students exploring how the use of algorithms and data might be understood, regulated and adjudicated by our legal system, with focus on machine learning and cryptographic algorithms.

Syllabus [link]

Course Numbers
BU: CS 791 / JD 673
UC Berkeley: CS 294

BU: Ran Canetti (CS), Stacey Dogan (Law), Aloni Cohen (CS & Law)
UC Berkeley: Shafi Goldwasser (CS), Frank Partnoy (Law)

Thursdays, Sep 5 to Dec 5
1:30–3:20pm pacific (UC Berkeley),
4:20–6:20pm eastern (BU)
UC Berkeley will hold an additional meeting on Thursday, Aug 29, 1:30-3:20pm pacific

BU: Law School, Room 204
UC Berkeley: Simons Institute, Room 116

Slides, Handouts, and Assignments [link]

Office Hours:
Aloni: Friday 9:00-10:30, Law 1404H.


The syllabus linked above will always be more up-to-date than this page. If they are inconsistent, trust the syllabus. Additional resources are available in the syllabus.


  • All students: Frankle & Ohm, Machine Learning [link]. This is a chapter from a textbook, “Computer Science for Lawyers”. Skip Sections 18.2.3 and 18.3.2, and the red-highlighted material. Depending on background, CS students may find this material very familiar.
  • CS students: Orin Kerr, How to Read a Legal Opinion [link]
  • All students: State v. Loomis, 881 N.W.2d 749 (Wis. 2016) [link]. Begin reading on p. 752. Read paragraphs 1-101, and 123-29 (concurring opinion).
  • All students: “COMPAS decision tree.txt” [link] and “COMPAS decision tree.png” [link].


  • All: Deborah Hellman, What is Discrimination? Plenary talk at FAT* 2018. [link]
  • All: Barocas & Selbst, Big Data’s Disparate Impact. Parts 1 and 2 (37 pages). [link]
  • All: Kleinberg, Mullainathan, & Raghavan, Inherent Trade-Offs in the Fair Determination of Risk Scores. Section 1 (8 pages). [link]
  • Additional Resources: The original ProPublica COMPAS article: “Machine Bias” [link] and the sequel “Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say” [link].


  • All students: Daphne Keller, Internet Platforms: Observations on Speech, Danger, and Money (Hoover Institution Aegis Series Paper No. 1807), [link] (pp. 1-28).
  • All students: Ellen P. Goodman & Ryan Whittington, Section 230 of the Communications Decency Act and the Future of Online Speech (German Marshall Fund, 2019), [link].


10/10 (updated 10/4/2019)


  • All: John M. Abowd, “The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century.” 8:16-22:00. [link]
  • All: U.S. Code Title 13, Section 9(a) [link]
  • Law Students: Wood et al. “Differential Privacy: A Primer for a Non-Technical Audience.” Sections I-III (pp 209-232). [link]
  • CS Students: Dwork and Roth, “Algorithmic Foundations of Differential Privacy.” Chapter 2 (pp 11-27). [link]


11/14: “Corporate Voting and Financial Algorithms”

All PDFs available on Piazza in a .zip file.

  • “Business Organizations” excerpts:
    • Ch 4, pages 71-83: basics of corporations
    • Ch 16, pages 399-402: basics of SH voting
    • Ch 26, pages 812-18: basics of securities trading
  • “WAIT” excerpt: pages 33-48: high frequency trading
  • “Encumbered Shares”: pages 775-81: share voting and economic interests
  • “US hedge fund activism”: pages 107, 110-13: Mylan and Telus examples