Law-and-Algorithms

Law and Algorithms

Spring 2024 Reading List

Thursdays, 2:10–4:10pm, BU Law Tower, Room 203

Please note that the readings in this course are highly likely to change over the course of the semester, though we try to avoid any changes to the readings less than a week before each class.

For the full syllabus, see here.

1. Modeling and Automation
   • Intro to Modeling and Automation (Jan. 18)
   • Automation Bias vs. Non-Automation Bias (Jan. 25)
   • Fairness in Automated Systems (Feb. 1)
2. Embodying Algorithms in Software
   • How Software is Constructed, Protected, and Examined (Feb. 8)
   • Putting Software On Trial (Feb. 15)
   • Creating an Ecosystem of Trustworthy Software (Feb. 22)
3. Layering in Secrecy
   • Anonymization, Identification, and Formalized Notions of Privacy (Feb. 29)
   • Bringing Formal Privacy to Public Administration (March 7)
   • The Weaponization of Privacy (March 21)
4. What Changes with Size?
   • Data Access as a Measurement of Power (March 28)
   • Harms in Machine Learning (April 4)
   • Correcting Harms in Machine Learning (April 11)
5. Law & Algorithms
   • Law and Algorithms (April 18)

Modeling and Automation

1. Intro to Modeling and Automation (Jan. 18)

Intro to Algorithms

• Brandeis Hall Marshall, “Computational Thinking in Practice,” from Data Conscience (2023) (circulated separately)
  • CDS students skim; Law students read from beginning, stopping at “Code Cloning” (pp. 87–97)

Intro to Law

• Andrew Sellars, A Practical Introduction to United States Law for Technologists (2023)
  • Law students skim; CDS students read Introduction and “The ‘Common Law’ System in the United States” (pp. 1–14), skim rest

The Social Construction of Data and Classification

• Sarah T. Roberts, “Your AI is a Human,” in Your Computer is on Fire (2021) (circulated separately)
  • read from beginning to end of “The Case of Commercial Content Moderation…” and from “Humans and AI in Symbiosis and Conflict” to end (pp. 51–58, 64–67)
• Anne L. Washington, “Source: Data are People, Too,” in Ethical Data Science (2023) (circulated separately)
  • read from beginning of chapter through end of “Whose Control?” (pp. 29–34)
• Geoffrey C. Bowker & Susan Leigh Star, “Introduction: To Classify is Human,” in Sorting Things Out (2000) (circulated separately)
  • read beginning of “Introduction: To Classify is Human,” stopping at “Investigating Infrastructure” (pp. 1–8)

What is Gained and What is Lost as Law Becomes Computational

• Joel Reidenberg, Lex Informatica: The Formulation of Policy Rules through Technology, 76 Tex. L. Rev. 553 (1997)
  • read Part IV (pp. 576–86) only
• Ari Ezra Waldman, Power, Process, and Automated Decision-Making, 88 Fordham L. Rev. 613 (2019)
  • read read Section I (pp. 616–22) only

Who is Centered

• Catherine D’Ignazio and Lauren F. Klein, “The Power Chapter,” from Data Feminism (2020)
  • read “Data Science by Whom?” and “Data Science for Whom?” only

Optional Reading

• Guidance About How to Cover Artificial Intelligence, Associated Press Stylebook (2023)
• Dave Karpf, Why Can’t Our Tech Billionaires Learn Anything New? (2023)
• Kristian Lum & Rumman Chowdhury, What is an “Algorithm?” It Depends Whom You Ask, MIT Tech. Review (Feb. 26, 2021)
• Tal Zarsky, The Trouble With Algorithmic Decisions, 41 Science, Technology, & Human Values 118 (2016)
• David Aurebach, The Stupidity of Computers, n+1 (2012)
• Langdon Winner, Do Artifacts Have Politics?, 109 Daedalus 121 (1980)

2. Automation Bias vs. Non-Automation Bias (Jan. 25)

Law’s Trouble with Numbers

• Edward K. Cheng, Fighting Legal Innumeracy, 17 Green Bag 2d 271 (2014)
  • read sections I and II (pp. 271–76) only.

To Model or Not To Model: The Case of Sentencing Guidelines

• Wendy Nelson Espeland & Berit Irene Vannebo, Accountability, Quantification, and Law, 3 Ann. Rev. L. & Social Sci. 21 (2007)
  • read “Federal Sentencing Guidelines” through the end of “Quantification in the Guidelines Approach” (pp. 25–33)

“Automation Bias”

• Danielle Keats Citron, Technological Due Process, 85 Wash. U. L. Rev. 1249 (2008)
  • read part I(B) only (pp. 1267–77)

“Automation Bias” in the Other Direction

• Christopher Robertson, Robophobia in Medicine
  • watch all
• S Mo Jones-Jang & Yong Jin Park, How do People React to AI Failure? Automation Bias, Algorithmic Aversion, and Perceived Controllability, 28 J. of Computer-Mediated Comm’n 1 (2022)
  • read “Literature Review” and “Discussion” (pp. 1–3, 6–7)

Where to Put the Humans

• Norman W. Spaulding, “Is Human Judgment Necessary? Artificial Intelligence, Algorithmic Governance, and the Law,” in The Oxford Handbook of Ethics of AI (2020)
  • read “Is Human Judgment Necessary?” (pp. 393–401)
• Rebecca Crootof, Margot Kaminski, & W. Nicholson Price II, Humans In the Loop, 76 Vand. L. Rev. 429 (2023)
  • read section III (pp. 460–73)

3. Fairness in Automated Systems (Feb. 1)

Defining Fairness

• Deborah Hellman, What is Discrimination, When is it Wrong, and Why?, Plenary talk at the Conference for Fairness, Accountability, and Transparency (2018)
  • watch the introduction to the talk (3:46–10:15)

Case Study: The COMPAS Algorithm

• Julia Angwin & Jeff Larson, Machine Bias, ProPublica (May 23, 2016)
  • read all
• State v. Loomis, 881 N.W.2d 749 (Wisc. 2016)
  • read excerpt

The Optimization Paradox

• Julia Angwin & Jeff Larson, Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say, ProPublica (Dec. 30, 2016)
  • read all
• Karen Hao & Jonathan Stray, Can You Make AI Fairer than a Judge? Play Our Courtroom Algorithm Game, MIT Technology Review (2019)
  • read all, and play with the interactive elements

Deeper Rethinking

• Ngozi Okidegbe, The Democratizing Potential of Algorithms?, 53 Conn. L. Rev. 739 (2022)
  • read part III only (pp. 767–77)
• Chelsea Barabas, Beyond Bias: Re-imagining the Terms of “Ethical AI” in Criminal Law, 12 Geo. J. L. & Mod. Crit. Race Persp. 83 (2020)
  • read Part VI (pp. 106–11) only

Optional Reading

• Sorelle A. Friedler, Carlos Scheidegger, & Suresh Venkatasubramanian, The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making, 64 Comm. of the ACM (2021)
• Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, Inherent Trade-Offs in the Fair Determination of Risk Scores, 43 ITCS 1 (2017)
• Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, and Adam Smith, From Soft Classifiers to Hard Decisions: How Fair can we Be?, FAT* (2019)
• Sandra G. Mayson, Bias In, Bias Out, 128 Yale L.J. 2219 (2019)
• Deborah Hellman, Measuring Algorithmic Fairness, 106 Va. L. Rev. 811 (2020)
• Aziz Huq, Racial Equity in Algorithmic Criminal Justice, 68 Duke L.J. 1043 (2019)

Embodying Algorithms in Software

4. How Software is Constructed, Protected, and Examined (Feb. 8)

How Software is Built, How Software Breaks

• Carl E. Landwehr et al., A Taxonomy of Computer Program Security Flaws, ACM Computing Surveys (1994)
  • read section 2.1.3 (pp. 219–21) only
• Ken Thompson, Reflections on Trusting Trust, Turing Award Lecture (1984)
  • read the section “Moral” (p. 763) only

How Should We Interpret Software?

• James Grimmelmann, The Structure and Legal Protection of Computer Programs, J. of Cross-Disciplinary Reas. in Comp. L. (2023)
  • read all

How Software is Protected in Law

• Sonia Katyal, The Paradox of Source Code Secrecy, 104 Cornell L. Rev. 1183 (2019)
  • read beginning of Part I through end of Part I(B) (pp. 1191–98) and Part I(D) (pp. 1207–10)

What Follows from Trade Secrecy

• Rebecca Wexler, Life, Liberty, and Trade Secrets, 70 Stan. L. Rev. 1343 (2018)
  • read from beginning of Part I to end of Part I(C) (pp. 1356–71)

5. Putting Software On Trial (Feb. 15)

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)

Case Study: TrueAllele

As a reminder, if you’re new to readling legal opinoins you might want to review Orin Kerr, How to Read a Legal Opinion, 11 Green Bag 2d 51 (2007).

• 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
• People v. Wakefield, 195 N.E.3d 19 (N.Y. 2022)
  • read excerpt

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

Optional

• 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 Institute (2019)

6. Creating an Ecosystem of Trustworthy Software (Feb. 22)

Note: joining us this week is Prof. Moon Duchin, who will discuss her own experience as an expert witness in a case that reached all the way to the Supreme Court.

Background on Racial Gerrymandering

• Racial Gerrymandering: Past Cases and the Supreme Court’s Upcoming Decision in Bethune-Hill II, Cong. Research Service (March 14, 2019)
  • read “The Supreme Court’s Racial Gerrymandering Jurisprudence” only (pp. 1–2)

Case Study: South Carolina NAACP v. Alexander (D.S.C. 2023)

• Expert Report of Moon Duchin (April 11, 2022)
  • read sections 1–8 (pp. 1–27)
• Defendants’ Motion In Limine to Exclude the Testimony of Moon Duchin (Sept. 2, 2022)
  • read all
• Plaintiffs’ Opposition to Motion in Limine to Exclude the Testimony of Moon Duchin (Sept. 9, 2022)
  • read all
• Amy Howe, Justices Question Finding that S.C. District Was Unconstitutional Racial Gerrymander, SCOTUSblog (Oct. 11, 2023)
  • read all

Optional:

• Joshua Kroll, Joanna Huey, Solon Barocas, Edward Felten, Joel Reidenberg, David Robinson, & Harlan Yu, Accountable Algorithms, 165 U. Penn. L. Rev. 633 (2017)
• 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)
• Kathleen A. Creel, Transparency in Complex Computational Systems, 87 Philosophy of Sci. 568 (2022)
• Mike Annany & Kate Crawford, Seeing Without Knowing: Limitations of the Transparency Ideal and its Application to Algorithmic Accountability, 20 New Media & Society 973 (2016)

Layering in Secrecy

7. Anonymization, Identification, and Formalized Notions of Privacy (Feb. 29)

Encryption Fundamentals

• What Should I Know About Encryption?, Electronic Frontier Foundation (last updated Nov. 24, 2018)
  • read all

What do lawyers mean when we say “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)
• María Angel & Ryan Calo, Distinguishing Privacy Law: A Critique of Privacy as Social Taxonomy, Colum. L. Rev. (forthcoming)
  • read Introduction (pp. 2–5)

Input privacy techniques in data science

• What Is Secure Multi-Party Computation?, Boston Univ. (Feb. 19, 2019)
  • watch all
• Amit Sahai, Computer Scientist Explains One Concept in 5 Levels of Difficulty: Zero-Knowledge Proofs, Wired (Jan. 18, 2022)
  • watch from beginning to 11:55

Output privacy techniques in data science

• Protecting Privacy with Math, Minute Physics (Sept. 12, 2019)
  • watch all

Putting legal and computational notions of privacy together

• 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 Part III only (pp. 713-34)

Optional

• 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 (2022)

8. Bringing Formal Privacy to Public Administration (March 7)

Note: joining us this week is Shlomi Hod, who will discuss a recent project involving use of differential privacy in government data.

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

Why did the Census adopt differential privacy?

• John M. Abowd et al., The 2010 Census Confidentiality Protections Failed, Here’s How and Why (2023)
  • read Abstract and Part I (pp. 1-4)

What happened when the Census adopted differential privacy?

• 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)
• 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 (pp. 1-2, 12)
• 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 (pp. 1-5 and 16)
• Mike Scheider & Morgan Lee, Tribal Nations Face Less Accurate, More Limited 2020 Census Data Because of Privacy Methods, Associated Press (Sept. 9, 2023)
  • read all

9. The Weaponization of Privacy (March 21)

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)
  • read “Preamble” (p. 1), “Differential Privacy” (pp. 20–21)

What Gets Hidden in Robust Privacy?

• Rory Van Loo, Privacy Pretexts, 108 Cornell L. Rev. 1 (2022)
  • read Introduction (pp. 2–12) and Part II (pp.22–38)

Case Study: NYU Ad Observatory and Meta

• Shannon Bond, NYU Researchers Were Studying Disinformation On Facebook. The Company Cut Them Off, NPR (Aug, 4, 2021)
  • read all
• Mike Clark, Research Cannot Be the Justification for Compromising People’s Privacy, Meta (Aug. 3, 2021)
  • read all
• Marshall Erwin, Why Facebook’s Claims About the Ad Observer are Wrong, Mozilla (Aug. 4, 2021)
  • read all
• Letter from Acting Director of the Bureau of Consumer Protection Samuel Levine to Facebook (Aug. 5, 2021)
  • read all

What Changes with Size?

10. Data Access as a Measurement of Power (March 28)

Note: joining us for part of the class this week is Prof. Allison McDonald, who will discusss some of her work addressing certain harmful outputs of generative AI.

Intro to Machine Learning

• Noah Yonack, A Non-Technical Introduction to Machine Learning, SafeGraph (March 3, 2017)
  • Read from beginning to end of “A framework for understanding ML” stopping before “A common regression model: simple linear regression”

How Data Informs AI

• Solon Barocas & Andrew Selbst, Big Data’s Disparate Impact, 104 Cal. L. Rev. 671 (2016)
  • read part I only (pp. 677–93)
• Emily Bender, Timnit Gebru, Angelina McMillan-Major, Margaret Mitchell, On the Dangers of Stochastic Parrots: Can Large Language Models be Too Big? 🦜, ACM Conf. on Fairness, Accountability, and Transparency (2021)
  • Read sections 1, 4, and 6-8. (pp. 610–11, 613–15, and 616–19)

Deeper Thinking About the Legal Environment of Data

• Amy Kapczynski, The Law of Informational Capitalism, 129 Yale L.J. 1460 (2020)
  • Read from intro to Part III to end of III(A) (pp. 1496–1508)

Optional

• Riana Pfefferkorn, Addressing Computer-Generated Child Sex Abuse Imagery: Legal Framework and Policy Implications, Lawfare (Feb. 5, 2024)
  • Please be aware that this article discusses CSAM in some detail.

11. Harms in Machine Learning (April 4)

How Bias Enters AI

• Ruha Benjamin, Race After Technology (2019)
  • read “Raising Robots,” (pp. 59–63), circulated separately.
• Zaid Khan & Yun Fu, One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision, ACM Conf. on Fairness, Accountability, and Transparency (2021)
  • read Sections 1, 3, 5.2, and 6
• Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, Aaron Rieke, Discrimination Through Optimization: How Facebook’s Ad Delivery Can Lead to Skewed Outcomes, ACM Conf. on Human-Computer Interaction (2019)
  • read Section 1 and the “Policy implications” section on pgs. 14-15

Intro to Anti-Discrimination Law

• The Civil Rights Act of 1964: An Overview, Cong. Research Serv. (2020)
  • read section on “Prohibitions Against Intentional and Disparate Impact Discrimination” under Title VII, stopping before “Unlawful Retaliation” (pp. 66–77)

Applicaiton to Algorithmic Systems

• Louis v. Saferent Solutions, LLC, — F. Supp. 3d — (D. Mass. 2023)
  • Read excerpt

Optional

• Colin Lecher & Maddy Varner, L.A.’s Scoring System for Subsidized Housing Gives Black and Latino People Experiencing Homelessness Lower Priority Scores, The Markup (Feb. 28, 2023)
• Emily Tucker, Artifice and Intelligence, Medium (March 8, 2022)
• Nancy Gertner, Loser’s Rules, 122 Yale L.J. Online 109 (2012)

12. Correcting Harms in Machine Learning (April 11)

Tensions in Disparate Impact and Disparate Treatment

• Ricci v. DeStephano, 557 U.S. 557 (2009)
  • read excerpt
• Pauline Kim, Race-Aware Algorithms: Fairness, Nondiscrimination and Affirmative Action, 110 Cal. L. Rev. 1539 (2022)
  • read Part IV (pp. 1574–86)

Technical Adjustments to Models

• Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy, Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices, FAT* (2020)
  • read parts 5 and 6 only (pp. 13–17)
• Michael Kim, Amirata Ghorbani, & James Zou, Multiaccuracy: Black-Box Post-Processing for Fairness in Classification, 2019 AIES 247 (2019)
  • read parts 1 and 4 (pp. 247–48, 250–51)

New Ways Forward

• Emily Black, John Logan Koepke, Pauline Kim, Solon Barocas, and Mingwei Hsu, Less Discriminatory Algorithms, 113 Geo. L.J. (forthcoming 2024).
  • Read part 1; the intro to part 2, 2(a), and 2(c); and part 4 (pp. 3–11, 13–15, and 26–34)

Optional

• Christopher Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2023)

Law and Algorithms

13. Law and Algorithms (April 18)

Note: joining us this week is Prof. Ngozi Okidegbe, the Moorman-Simon Interdisciplinary Career Development Associate Professor of Law and Assistant Professor of Computing & Data Sciences at Boston University. Prof. Okidegbe will be discussing the draft of a new article, which will be circulated via Teams as this week’s required reading.