COURSE EVALUATION LINK
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.
PLEASE NOTE: DUE TO THE CROSS LISTING OF THIS COURSE, THERE ARE SOME STRANGE LOGISTICS TO BE AWARE OF! MAKE SURE TO READ THE FOLLOWING!
- THIS COURSE IS SCHEDULED FROM 2:10-4:10 in the Law Tower Room 203. Note that this is different than the listed time and location in student link for DS457/DS657
- This course follows the Law School's Academic Calendar. That means that the first class period is on Thursday, January 19.
The syllabus for this semester (Spring 2023) can be found here. Please note that the syllabus currently lists the wrong room. We will be in Room 203.
Links to jump down to the reads for a particular class
- Class 1
- Class 2
- Class 3
- Class 4
- Class 5
- Class 6
- Class 7
- Class 8
- Class 9
- Class 10
- Class 11
- Class 12
- Class 13
Class 1: Intro to Law and Algorithms (Jan. 19)
Intro to Computational Thinking:
- Stanford Encyclopedia of Philosophy, The Philosophy of Computer Science. CS students can skim, as it will be largely familiar to you already. Law Students read Section 2 ("Intention and Specification").
- Jeannette M. Wing, Computational Thinking: What and Why? (2010) - read all.
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 introduction and "The 'Common Law' System in the United States" (pp. 1-14), and skim the rest.
The Opportunities of Law & Algorithms:
- Joel Reidenberg, Lex Informatica: The Formulation of Policy Rules through Technology, 76 Tex. L. Rev. 553 (1997) – read Part IV (pp. 576–86) only
The Collision of Law & Algorithms:
- Ari Ezra Waldman, Power, Process, and Automated Decision-Making, 88 Fordham L. Rev. 613 (2019) – read Section I only
- Hannah Bloch-Wehba, Access to Algorithms, 88 Fordham L. Rev. 1265 (2020) – read Part II (pp. 1290–95) only
- Edward K. Cheng, Fighting Legal Innumeracy, 17 Green Bag 2d 271 (2014) – read section II only.
What Lies Underneath Technological Governance:
- 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?”
- Shannon Vallor, We Used to Get So Excited about Technology. What Happened?, MIT Tech. Review (Oct. 21, 2022)
- Kristian Lum & Rumman Chowdhury, What is an “Algorithm?” It Depends Whom You Ask, MIT Tech. Review (Feb. 26, 2021)
- Ruha Benjamin, 2020 Vision: Reimagining the Default Settings of Technology & Society (2020)
- Alan R. Wagner, Jason Borenstein, Ayanna Howard, Overtrust in the Robotic Age, Comm. of the ACM (Sept. 2018) "Overtrust in the Robotic Age"
- Tal Zarsky, The Trouble With Algorithmic Decisions, 41 Science, Technology, & Human Values 118 (2016)
- David Aurebach, The Stupidity of Computers, n+1 (2012)
- Danielle Keats Citron, Technological Due Process, 85 Wash. U.L. Rev. 1249 (2008)
- Langdon Winner, Do Artifacts Have Politics?, 109 Daedalus 121 (1980)
- Plus many, many more readings to come in subsequent classes!
Class 2 – The Development and Legal Protection of Software (Jan. 27)
How Software is Built, How Software Breaks
- All About Software, How does a compiler, interpreter, and CPU work?
- James Grimmelman (with Solon Barocas), CPU, Esq: Should Law Be More Like Software?, DIMACS Workshop on Co-development of CS and Law (2020) — watch 19:30 through 27:15
Concepts of Verification and Validation
- James A. Whittaker. What Is Software Testing? And Why Is It So Hard?
How Software is Protected in Law
- If you are new to field of intellectual property, review these introductory concepts from the Digital Media Law Project:
How Software is Examined in Litigation
- Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, and Rebecca Wexler, Adversarial Scrutiny of Evidentiary Statistical Software, ACM FAccT (2022) – read part 2 (pp. 3–5) only
- Sergey Bratus, Ashlyn Lembree, and Anna Shubina, “Software on the Witness Stand: What Should it Take for Us to Trust It?,” in Trust and Trustworthy Computing (Alessandro Acquisti, Sean W. Smith, and Ahmad-Reza Sadeghi eds. 2010) – read Section 4 (pp. 402–07) and Section 7 (pp. 409–15)
Tensions in Intellectual Property and Software Scrutiny
- Rebecca Wexler, Life, Liberty, and Trade Secrets, 70 Stan. L. Rev. 1343 (2018) – read Part I (pp. 1356–77)
- Ken Thompson, Reflections on Trusting Trust, Turing Award Lecture (1984).
- Zeynep Tufekci, The Shameful Open Secret Behind Southwest’s Failure, N.Y. Times (Dec. 31, 2022)
- Sonia Katyal, The Paradox of Source Code Secrecy, 104 Cornell L. Rev. 1183 (2019)
- Radiolab, Bit Flip, Originally aired May 2019
Class 3 – Putting the TrueAllele Algorithm on Trial (Feb. 2)
TrueAllele on Trial
- People v. Superior Court (Chubbs) (Cal. Ct. App. Jan. 9, 2015) – read excerpt
- State v. Pickett, 246 A.3d 279 (N.J. App. Div. 2021) – read excerpt
Transparency as a Solution and its Critics
- Kathleen A. Creel, Transparency in Complex Computational Systems, 87 Philosophy of Sci. 568 (2022) – read part III only (pp. 572–582) only
- Mike Annany & Kate Crawford, Seeing Without Knowing: Limitations of the Transparency Ideal and its Application to Algorithmic Accountability, 20 New Media & Society 973 (2016) – read “Limits of the transparency ideal” section, pp. 977–82
- Steven Bellovin, Matt Blaze, Susan Landau, & Brian Owsley, Seeking the Source: Criminal Defendants’ Constitutional Right to Source Code, 17 Ohio State Tech. L.J. 1 (2021) – read section II(D) only, pp. 31–40
- Joshua Kroll, Joanna Huey, Solon Barocas, Edward Felten, Joel Reidenberg, David Robinson, & Harlan Yu, Accountable Algorithms, 165 U. Penn. L. Rev. 633 (2017) – read parts II(A) and II(B) only, pp. 657–62
The Limits of Transparency Through Adversarial Litigation:
- Order on Interpretation of Protective Order, Houston Fed. of Teachers v. Houston Indep. Sch. Dist. (S.D. Tex. March 11, 2016) – read excerpt
- Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, and Rebecca Wexler, Adversarial Scrutiny of Evidentiary Statistical Software, ACM FAccT (2022) – read parts 4 and 5 (pp. 6–15) only
- Andrea Roth, Machine Testimony, 126 Yale L.J. 1972 (2017)
- NIST Publishes Review of DNA Mixture Interpretation Methods, NIST (June 9, 2021)
- Rashida Richardson, Jason M. Schultz, and Vincent M. Sutherland, Litigating Algorithms: 2019 Report, AI Now (2019)
- Todd Feathers, Why It’s So Hard to Regulate Algorithms, The Markup (Jan. 4, 2022).
- Paul W. Grimm, Maura Grossman, and Gordon Cormack, Artificial Intelligence as Evidence, 19 Nw. J. Tech. & Intell. Prop. 9 (2021).
- Shira Schoenberg, Breathalyzer Scandal Could Reopen 27,000 Drunk Driving Cases, Commonwealth (Nov. 27, 2022).
Class 4 – Foundational Concepts in Privacy and Secrecy (Feb. 9)
Introduction to Secrecy in Computing
- CIA Triad, Wikipedia -- read section labeled "Basic Principles"
- Bruce Schneier, The Security Mindset
Basic cryptography: encryption, signatures, and hashing
- EFF Surveillance Self-Defense Guide – read the following subsections:
- Glencora Borradaile, Defend Dissent: Digital Suppression and Cryptographic Defense of Social Movements. Read the following sections:
Advanced applications: secure multi-party computation and zero-knowledge proofs
- Mayank Varia, Cryptographically Secure Data Analysis for Social Good (2018).
- Amit Sahai, Computer Scientist Explains One Concept in 5 Levels of Difficulty, by WIRED (2022). Watch the full video.
- Kenneth A. Bamberger, Ran Canetti, Shafi Goldwasser, Rebecca Wexler, & Evan Zimmerman, Verification Dilemmas, Law, and The Promise of Zero-Knowledge Proofs, 37 Berkeley Tech. L. J. 101 (2023) -- Read Part III only (pp. 127–38)
Secrecy vs. Privacy
- Daniel Solove, Understanding Privacy (2008) – read introduction to Chapter 1 (pp. 1–2); “The Concept of Privacy” (pp. 6–8); and “A New Theory of Privacy” (pp. 8–11)
Who is Centered in Cryptography Policy?
- Seny Kamara, Crypto For the People (2020) – watch 7:11 to 21:14
- Philip Rogaway, The Moral Character of Cryptographic Work (2015)
- Orin S. Kerr & Bruce Schneier, Encryption Workarounds, 106 Geo. L. J. 989 (2018)
- Aloni Cohen & Sunoo Park, Compelled Decryption and the Fifth Amendment: Exploring the Technical Boundaries, 32 Harv. J. L. & Tech. 169 (2018)
- Gabriel Kaptchuk, Secure Multiparty Compuation FAQ for Non-Experts (2022)
Class 5 – Trust in Voting Architecture, Trust in Vote Tallies (Feb. 16)
Voting Systems and Their Requirements
- Sunoo Park, The Right to Vote Securely, Colo. L. Rev. (forthcoming 2023) – Read Parts II(A) and II(B) (pp. 17–23) and Part III(A) (pp. 33–38) (Document circulated via Teams)
- Svetlana Lowry & Poorvi Vora, Desirable Properties in Voting Systems, NIST (Sept. 25, 2009) – read section 2 only
The Infusion of Technology in Voting Systems
- Matthew Blaze, Cryptography and Elections: Threat or Menace. Real World Cryptography 2019. — Watch talk (22:38–53:00)
- Ron Rivest, John Wack On the Notion of "Software Independence" in Voting Systems (2006) - read Sections 1-3
Validating and Challenging Elections
- Lake v. Hobbs (D. Ariz. Aug. 26, 2022) – read excerpt
- Douglas Jones, A Brief Illustrated History of Voting (2003)
- Holly Ann Garnett & Pam Simpson, American Trust in Voting Technology (2019)
- Kevin Anthony Hoff & Masooda Bashir, Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust, 57 Human Factors 407 (2015)
- Michael A. Specter, James Koppel, Daniel Weitzner, The Ballot is Busted Before the Blockchain: A Security Analysis of Voatz, the First Internet Voting Application Used in U.S. Federal Elections, USENIX Security (2020)
- Jill Lapore, Rock, Paper, Scissors, New Yorker (Oct. 6, 2008)
The Challenge of “Proof of Inclusion” (Feb 23)
The majority of your reading this week will be the background research on the voting system that you will be analyzing for Assignment 2. Please come prepared to discuss your system.
Intro to E2EV systems
- Josh Benaloh, Ronald Rivest, Peter Y. A. Ryan, Philip Stark, Vanessa Teague, Poorvi Vora, End-to-End Verifiability (Feb. 2014) – read all
- Matthew Bernhard, Josh Benaloh, J. Alex Halderman, Ronald L. Rivest, Peter Y. A. Ryan, Philip B. Stark, Vanessa Teague, Poorvi L. Vora, and Dan S. Wallach, Public Evidence from Secret Ballots (Aug. 2017) – read Part 4 only
The Adoption of E2EV Systems
- Benjamin Wofford, A Texas County Clerk’s Bold Crusade to Transform How We Vote, Wired (Sept. 15, 2020) – read all
Class 7 – Anonymization, Identification, and Differential Privacy (March 2)
Intro to Anonymization and Re-Identification Attacks
- Paul Ohm, Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization, 57 UCLA L. Rev. 1701 (2010) — read Introduction (pp. 1703–06), Part II(A)(3) through II(B) (pp. 1735–1741)
Intro to Differential Privacy
- What Is Differential Privacy?, NIST (July 3, 2019) – watch all (about 3 mins)
- Alexandra Wood, Micah Altman, Aaron Bembenek, Mark Bun, Marco Gaboardi, James Honaker, Kobbi Nissim, David R. O’Brien, Thomas Steinke & Salil Vadhan, Differential Privacy: A Primer for a Non-Technical Audience, 21 Vand. J. Ent. & Tech. L. 209 (2018) — Read executive summary (pp.211-214)
- Joseph Near, David Darais, & Kaitlin Boeckl, Differential Privacy for Privacy-Preserving Data Analysis: An Introduction to our Blog Series, NIST (July 27, 2020) – read all
Bringing Differential Privacy Into the Law
- Kobbi Nissim, Aaron Bembenek, Alexandra Wood, Mark Bun, Marco Gaboardi, Urs Gasser, David R. O’Brien, Thomas Steinke, & Salil Vadhan, Bridging the Gap Between Computer Science and Legal Notions of Privacy, 31 Harv. J. Law & Tech. 687 (2018) – read the beginning of Part III(B) and Part III(B)(1) (pp. 718–24); and Part III(C) (pp. 730–33)
- Rachel Cummings, Gabriel Kaptchuk, Elissa Redmiles, "I need a better description": An Investigation Into User Expectations For Differential Privacy, ACM Conf. on Computer and Comms. Security (2021) – read parts 1, 5 and 6
- Cynthia Dwork & Aaron Roth, The Algorithmic Foundations of Differential Privacy (2014)
- Josep Domingo-Ferrer, David Sánchez, Alberto Blanco-Justicia, The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning), Communications of the ACM (2021)
Bringing Differential Privacy to the Census (March 16)
Utility and Privacy in the Census
- 13 U.S.C. § 141 – read excerpt
- 13 U.S.C. § 9 – read excerpt
- Pub. L. 105-119 § 209 (Nov. 26, 1997) – read excerpt
Differential privacy in the 2020 Census
- Gordon Long, Consistency of Data Products and Formal Privacy Methods for the 2020 Census, MITRE (2022) – read Executive Summary (pages 1–9)
- danah boyd and Jayshree Sarathy, Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau’s Use of Differential Privacy, Harvard Data Science Review (2022) – read the Introduction (pp. 3–5) and Sections 3–4 (pp. 8–17)
Legal Challenges to Differential Privacy
- Alabama v. Dep’t of Commerce, 546 F. Supp. 3d 1057 (M.D. Ala. 2021) – read excerpt
- Amicus Brief of Data Privacy Experts, Alabama v. Dep’t of Commerce, 546 F. Supp. 3d 1057 (M.D. Ala. 2021) – read Part II (pp. 13–17)
From Privacy to Bias
- Miranda Christ, Sarah Radway, and Steve Bellovin, Differential Privacy and Swapping: Examining De-Identification’s Impact on Minority Representation and Privacy Preservation in the U.S. Census. IEEE Symposium on Security and Privacy (2022) – read parts 1 and 5
- Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal. Census TopDown: The Impacts of Differential Privacy on Redistricting (2022) – read Sections 1, 2, and 8 (pages 1-5 and 16)