7 papers
Private Again: Artificial Intelligence Agents Restore Anonymity---Foreclosing Discrimination and Its Proof
Anirban Mukherjee, Hannah Hanwen Chang
Artificial intelligence agents can transact online on behalf of a human principal---browsing, paying, receiving, and reviewing---without revealing who that principal is. That archi…
Copyright Laundering Through the AI Ouroboros: Adapting the 'Fruit of the Poisonous Tree' Doctrine to Recursive AI Training
Anirban Mukherjee, Hannah Hanwen Chang
Copyright enforcement rests on an evidentiary bargain: a plaintiff must show both the defendant's access to the work and substantial similarity in the challenged output. That barga…
Fluid Agency in AI Systems: A Case for Functional Equivalence in Copyright, Patent, and Tort
Anirban Mukherjee, Hannah Hanwen Chang
Modern Artificial Intelligence (AI) systems lack human-like consciousness or culpability, yet they exhibit fluid agency: behavior that is (i) stochastic (probabilistic and path-dep…
Beyond Pairwise Comparisons: A Distributional Test of Distinctiveness for Machine-Generated Works in Intellectual Property Law
Anirban Mukherjee, Hannah Hanwen Chang
Key doctrines, including novelty (patent), originality (copyright), and distinctiveness (trademark), turn on a shared empirical question: whether a body of work is meaningfully dis…
Charting the Parrot's Song: A Maximum Mean Discrepancy Approach to Measuring AI Novelty, Originality, and Distinctiveness
Anirban Mukherjee, Hannah Hanwen Chang
Current intellectual property frameworks struggle to evaluate the novelty of AI-generated content, relying on subjective assessments ill-suited for comparing effectively infinite A…
Stochastic, Dynamic, Fluid Autonomy in Agentic AI: Implications for Authorship, Inventorship, and Liability
Anirban Mukherjee, Hannah Hanwen Chang
Agentic Artificial Intelligence (AI) systems, exemplified by OpenAI's DeepResearch, autonomously pursue goals, adapting strategies through implicit learning. Unlike traditional gen…