When: Third Friday of each month at 1 PM Central Time (sometimes fourth Friday; next workshop: Wednesday, February 25, 1:00 to 3:00 p.m. Central Time).
What: First 90 minutes: Two presentations of CS+Law works in progress or new papers with open Q&A. Last 30 minutes: Networking.
Where: Zoom
Who: CS+Law faculty, postdocs, PhD students, and other students (1) enrolled in or who have completed a graduate degree in CS or Law and (2) engaged in CS+Law research intended for publication.
A Steering Committee of CS+Law faculty from Berkeley, Boston U., U. Chicago, Cornell, Georgetown, MIT, North Carolina Central, Northwestern, Ohio State, Penn, Technion, and UCLA organizes the CS+Law Monthly Workshop. A different university serves as the chair for each monthly program and sets the agenda.
Why: The Steering Committee’s goals include building community, facilitating the exchange of ideas, and getting students involved. To accomplish this, we ask that participants commit to attending regularly.
Computer Science + Law is a rapidly growing area. It is increasingly common that a researcher in one of these fields must interact with the other discipline. For example, there is significant research in each field regarding the law and regulation of computation, the use of computation in legal systems and governments, and the representation of law and legal reasoning. There has been a significant increase in interdisciplinary research collaborations between researchers from CS and Law. Our goal is to create a forum for the exchange of ideas in a collegial environment that promotes building community, collaboration, and research that helps to further develop CS+Law as a field.
Please join us for our next CS+Law Research Workshop online on Wednesday, February 25, 1:00 to 3:00 p.m. Central Time (Chicago Time).
Workshop 36 Organizer: Columbia (Rachel Cummings) and Tel Aviv University (Inbal Talgam-Cohen)
Please note a slightly different format:
We are joining forces for this workshop with another interdisciplinary community that combines CS and Economics – an ACM community known as SIGecom. Their annual virtual event will be dedicated this year to CS+Law. You will notice some familiar names among the speakers. The main differences will be:
(1) Registration by a (super-brief) Google form
(2) A different platform, called Virtual Chair, which allows for virtual mingling before the talks – it’s intuitive to use (and we’ll provide a guide)
Agenda:
30-minute informal mingling on Virtual Chair
20-minute presentation – Aloni Cohen
10-minute Q&A
20-minute presentation – Rebecca Wexler
10-minute Q&A
20-minute presentation – Juba Ziani
10-minute Q&A
Presentation 1: Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
Presenter: Aloni Cohen, Assistant Professor of Computer Science and Data Science at the University of Chicago
Abstract:
Are there any conditions under which a generative model’s outputs are guaranteed not to infringe the copyrights of its training data? This is the question of “provable copyright protection” first posed in [VyasKB23]. They define near access-freeness (NAF) and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection—foundations that are firmer both technically and legally. First, we show that NAF alone does not prevent infringement. In fact, NAF models can enable verbatim copying, a blatant failure of copy protection that we dub being tainted. Then, we introduce our blameless copy protection framework for defining meaningful guarantees, and instantiate it with clean-room copy protection. Clean-room copy protection allows a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual “clean-room setting.” Finally, we formalize a common intuition about differential privacy and copyright by proving that DP implies clean-room copy protection when the dataset is golden, a copyright deduplication requirement.
Presentation 2: AI Suppression: E-Discovery Software and Brady
Presenter: Rebecca Wexler, Professor of Law at Columbia Law School
Abstract:
Prosecutors regularly rely on AI e-discovery software, known as technology assisted review (TAR) tools, to sort and prioritize digital evidence. These tools implicate constitutional concerns: they can either risk suppressing or help to surface exculpatory and impeachment evidence that prosecutors must disclose under the Bradydue process rule. Yet doctrine, agency guidance, and scholarship offer virtually no direction on this problem. This Article examines how TAR may affect Brady compliance. Using original computer science simulations, we show that TAR can either hide or help to expose Brady evidence, depending on how it is configured. From these results we derive three recommendations: prosecutors should run TAR separately for inculpatory and exculpatory/impeachment evidence; courts should permit TAR coding of Brady material even when active searching is constitutionally contested; and procurement guidelines should favor flexible classifiers. More broadly, our examination of TAR highlights unresolved tensions in Brady doctrine: whether liability attaches when the prosecution possesses but does not know about exculpatory or impeachment evidence; how Bradyinteracts with Fourth Amendment privacy protections; and whether Brady should be limited to preventing suppression or expanded into a full duty to assist defense investigations.
Presentation 3: Algorithmic Collusion Without Threats
Presenter: Juba Ziani, Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech
Abstract: There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors who refuse to support high prices, and these strategies can be automatically learned. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to optimize their payoff. Is this intuition correct? Would preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can emerge even when both players are using algorithms which do not encode threats, and which optimize for their own revenue. We study sequential pricing games in which a first mover deploys an algorithm and then a second mover optimizes within the resulting environment. We show that if the first mover deploys any algorithm with a no-regret guarantee, and then the second mover even approximately optimizes within this now static environment, monopoly-like prices arise. The result holds for any no-regret learning algorithm deployed by the first mover and for any pricing policy of the second mover that obtains them profit at least as high as a random pricing would -- and hence the result applies even when the second mover is optimizing only within a space of non-responsive pricing distributions which are incapable of encoding threats. In fact, there exists a set of strategies, neither of which explicitly encode threats that form a Nash equilibrium of the simultaneous pricing game in algorithm space, and lead to near monopoly prices. This suggests that the definition of ``algorithmic collusion'' may need to be expanded, to include strategies without explicitly encoded threats.
Join our group to get the agenda and Zoom information for each meeting and engage in the CS+Law discussion.
Submit a proposed topic to present. We strongly encourage the presentation of works in progress, although we will consider the presentation of more polished and published projects.
Friday, September 20, 1:00 to 3:00 p.m. Central Time (Organizer: Northwestern)
Friday, October 18, 1:00 to 3:00 p.m. Central Time (Organizer: UC Berkeley)
Friday, November 15, 1:00 to 3:00 p.m. Central Time (Organizer: University of Chicago)
Friday, January 17, 1:00 to 3:00 p.m. Central Time (Organizer: UPenn)
Friday, February 21, 1:00 to 3:00 p.m. Central Time (Organizer: Cornell)
Friday, March 21, 1:00 to 3:00 p.m. Central Time (Organizer: Tel Aviv University + Harvard)
Friday, April 18, 1:00 to 3:00 p.m. Central Time (Organizer: TBD)
Friday, May 16, 1:00 to 3:00 p.m. Central Time (Organizer: Georgetown)
Ran Canetti (Boston U.)
Bryan Choi (Ohio State)
Aloni Cohen (U. Chicago)
April Dawson (North Carolina Central)
James Grimmelmann (Cornell Tech)
Jason Hartline (Northwestern)
Dan Linna (Northwestern)
Paul Ohm (Georgetown)
Pamela Samuelson (Berkeley)
Inbal Talgam-Cohen (Technion - Israel Institute of Technology)
John Villasenor (UCLA)
Rebecca Wexler (Berkeley)
Christopher Yoo (Penn)
Northwestern Professors Jason Hartline and Dan Linna convened an initial meeting of 21 CS+Law faculty at various universities on August 17, 2021 to propose a series of monthly CS+Law research conferences. Hartline and Linna sought volunteers to sit on a steering committee. Hartline, Linna, and their Northwestern colleagues provide the platform and administrative support for the series.