12 citations · 17 across the 12 of their papers we have counts for
12 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…
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…
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…
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…
Agentic AI: Autonomy, Accountability, and the Algorithmic Society
Anirban Mukherjee, Hannah Hanwen Chang
Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn workflows. Unlike traditional generative AI, which res…
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Anirban Mukherjee, Hannah Hanwen Chang
Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that as…
Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption
Anirban Mukherjee, Hannah Hanwen Chang
Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguis…
Silico-centric Theory of Mind
Anirban Mukherjee, Hannah Hanwen Chang
Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these ment…
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables
Anirban Mukherjee, Hannah H. Chang
We propose a novel framework for incorporating qualitative data into quantitative models for causal estimation. Previous methods use categorical variables derived from qualitative…
Machine Learning and Consumer Data
Hannah H. Chang, Anirban Mukherjee
The digital revolution has led to the digitization of human behavior, creating unprecedented opportunities to understand observable actions on an unmatched scale. Emerging phenomen…