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Sat Jul 29 12:00 PM -- 08:00 PM (PDT) @ Ballroom B None
Generative AI and Law (GenLaw)
Katherine Lee · A. Feder Cooper · FatemehSadat Mireshghallah · Madiha Zahrah · James Grimmelmann · David Mimno · Deep Ganguli · Ludwig Schubert

Workshop Home Page

Progress in generative AI depends not only on better model architectures, but on terabytes of scraped Flickr images, Wikipedia pages, Stack Overflow answers, and websites. But generative models ingest vast quantities of intellectual property (IP), which they can memorize and regurgitate verbatim. Several recently-filed lawsuits relate such memorization to copyright infringement. These lawsuits will lead to policies and legal rulings that define our ability, as ML researchers and practitioners, to acquire training data, and our responsibilities towards data owners and curators.

AI researchers will increasingly operate in a legal environment that is keenly interested in their work — an environment that may require future research into model architectures that conform to legal requirements. Understanding the law and contributing to its development will enable us to create safer, better, and practically useful models.

We’re excited to share a series of tutorials from renowned experts in both ML and law and panel discussions, where researchers in both disciplines can engage in semi-moderated conversation.

Our workshop will begin to build a comprehensive and precise synthesis of the legal issues at play. Beyond IP, the workshop will also address privacy and liability for dangerous, discriminatory, or misleading and manipulative outputs. It will take place on 29 July 2023, in Ballroom B.

Welcome and Opening Remarks (Opening)
Invited Talk: Pam Samuelson (Invited Talk)
Invited Talk: Mark Lemley (Invited Talk)
Coffee Break (Break)
Invited Talk: Miles Brundage (Invited Talk)
Panel Discussion on Intellectual Property (Panel)
Lunch Break (Break)
Invited Talk: Jack Balkin (Invited Talk)
Spotlight Presentations (5 Papers) (Talk)
In Person Poster Session (Poster)
Coffe Break (Break)
Invited Talk: Nicholas Carlini (Invited Talk)
Invited Talk: Gautam Kamath (Invited Talk)
Panel Discussion on Privacy (Panel)
Title: Ignore the Law: The Legal Risks of Prompt Injection Attacks on Large Language Models; Author(s): Ram Shankar Siva Kumar, Jonathon Penney (Poster)
Title: Chain Of Reference prompting helps LLM to think like a lawyer Author(s): Nikon Rasumov-Rahe, Aditya Kuppa, Marc Voses (Poster)
Title: Compute and Antitrust: Regulatory implications of the AI hardware supply chain, from chip design to foundation model APIs; Author(s): Haydn Belfield, Shin-Shin Hua (Poster)
Title: Consent-to-train Metadata for a Machine Learning World; Author(s): Daphne E Ippolito, Yun William Yu (Poster)
Title: Licensing Training Data and Attributing Copyright of Derivative Content From Large Language Models Can Resolve Up- and Downstream Copyright Issues; Author(s): Brian L Zhou, Lakshmi Sritan R Motati (Poster)
Title: How can we manage the risks and liabilities associated with legal translation in the age of machine translation and generative AI?; Author(s): Argyri Panezi, John O Shea (Poster)
Title: The Limited Relevance of Fair Use: Legal Implications of Training LLMs on Copyrighted Text; Author(s): Noorjahan Rahman (Poster)
Title: Applying Torts to Juridical Persons: Corporate and AI Governance; Author(s): Aaron Tucker (Poster)
Title: Differential Privacy vs Detecting Copyright Infringement: A Case Study Using Normalizing Flows; Author(s): Saba Amiri, Eric Nalisnick, Adam Belloum, Sander Klous, Leon Gommans (Poster)
Talk (Recorded Talk)
Title: Protecting Visual Artists from Generative AI: A Multidisciplinary Perspective; Author(s): Eunseo Choi (Poster)
Title: The Restatement (Artificial) of Torts; Author(s): Colin Doyle (Poster (Spotlight))
Title: The Data Provenance Initiative; Author(s): Shayne Longpre, et al. (Poster (Spotlight))
Title: Diffusion Art or Digital Forgery? Investigating Data Replication in Stable Diffusion; Author(s): Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas A. Geiping, Tom Goldstein (Poster (Spotlight))
Title: When is Copying Fair? Exploring the Copyright Implications of Andy Warhol Foundation v. Goldsmith for Generative AI; Author(s): Tiffany Georgievski (Poster)
Title: Break It Till You Make It: Limitations of Copyright Liability Under a Pre-training Paradigm of AI Development; Author(s): Rui-Jie Yew, Dylan Hadfield-Menell (Poster (Spotlight))
Title: Measuring the Success of Diffusion Models at Imitating Human Artists; Author(s): Stephen Casper, et al. (Poster (Spotlight))
Title: Gradient Surgery for One-shot Unlearning on Generative Model; Author(s): Seohui Bae, Seoyoon Kim, Hyemin Jung, Woohyung Lim (Poster)
Title: Anticipating and Mitigating Unsafe and Harmful Outcomes with Generative Language Models: The Role and Limits of Laws; Author(s): Inyoung Cheong, Aylin Caliskan, Tadayoshi Kohno (Poster)
Title: Machine Learning Has A Fixation Problem; Author(s): Katrina Geddes (Poster)
Title: From Algorithmic Destruction to Algorithmic Imprint: Generative AI and Privacy Risks Linked to Potential Traces of Personal Data in Trained Models; Author(s): Lydia Belkadi, Catherine Jasserand (Poster)
Title: Developing Methods for Identifying and Removing Copyrighted Content from Generative AI Models; Author(s): Krishna Sri Ipsit Mantri, Nevasini NA Sasikumar (Poster)
Title: Takeaways from Data Extraction and Unlearning for Law; Author(s): Jaydeep Borkar (Poster)
Title: AI and the EU Digital Markets Act: Addressing the Risks of Bigness and Dominance in Generative AI; Author(s): Andrew Chong, et al. (Poster)
Title: Generative AI and the Future of Financial Advice Regulation; Author(s): Talia Gillis, Sarith Felber, Itamar Caspi (Poster)
Title: Exploring Antitrust and Platform Power in Generative AI; Author(s): Konrad Kollnig, Qian Li (Poster)
Title: PoT: Securely Proving Legitimacy of Training Data and Logic for AI Regulation; Author(s): Hongyang Zhang, Haochen Sun (Poster)
Title: When Synthetic Data Met Regulation; Author(s): Georgi Ganev (Poster)
Title: Provably Confidential Language Modelling Author(s): Xuandong Zhao, Lei Li, Yu-Xiang Wang (Poster)
Title: The Extractive-Abstractive Axis: Measuring Content ’Borrowing’ in Generative Language Models; Author(s): Nedelina Teneva (Poster)
Title: Reclaiming the Digital Commons: A Public Data Trust for Training Data; Author(s): Alan Chan, Herbie Bradley, Nitarshan Rajkumar (Poster)