8 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…
Operational Agency: A Permeable Legal Fiction for Tracing Culpability in AI Systems
Anirban Mukherjee, Hannah Hanwen Chang
Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood-a paradox that fractures doctrines grounded in human-centric notions of…
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…