Scaling Laws: Algorithmic Disgorgement with Christina Lee

Christina Lee, Visiting Associate Professor of Law and Privacy and Technology Law Fellow at George Washington University Law School, joins Kevin Frazier, director of the AI Innovation and Law Program at the University of Texas School of Law and a senior fellow at the Abundance Institute, from the Institute of Law and AI’s Workshop on Law-Following AI in Cambridge, UK.

They discuss her forthcoming article, Beyond Algorithmic Disgorgement: Remedying Algorithmic Harms. 

The conversation begins with the basics: what is algorithmic disgorgement, why did the FTC first use it in the Cambridge Analytica case, and how has the remedy evolved from data-deletion-plus-model-deletion orders into a much broader candidate remedy for AI harms? Christina explains why the remedy’s expansion matters, especially after the FTC’s Rite Aid settlement, where the alleged wrong was not unlawful data collection but the allegedly unfair use of facial recognition technology.

They also discuss alternative remedies, including injunctions, post-processing, fine-tuning, machine unlearning, and training-data attribution. Finally, they wrap up with Christina’s professional journey and the future of legal education in the age of AI.