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Anirban Mukherjee

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.CY2
same name
  • Anirban Mukherjee — 10 papers, h 6
  • Anirban Mukherjee — 6 papers, h 3
  • Anirban Mukherjee — 5 papers, h 2
  • Anirban Mukherjee — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedSafeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CY2026

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…

cs.AI2024

AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research

Anirban Mukherjee, Hannah Hanwen Chang

We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published af…

cs.AI2024

Psittacines of Innovation? Assessing the True Novelty of AI Creations

Anirban Mukherjee

We examine whether Artificial Intelligence (AI) systems generate truly novel ideas rather than merely regurgitating patterns learned during training. Utilizing a novel experimental…

cs.CY2024★ 1 cited

Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation

Anirban Mukherjee

Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be us…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.