mlsanityData to 14 Oct 2026

Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships

arXiv 2505.16899 ↗First posted 22 May 2025cs.AIcited by 3

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Abstract (arXiv)

Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of collaboration and extended cognition, yet they also pose major risks-including and beyond risks of typical AI tools and agents. In this commentary, we systematically identify risks of AI thought partners through a novel framework that identifies risks at multiple levels of analysis, including Real-time, Individual, and Societal risks arising from collaborative cognition (RISc). We leverage this framework to propose concrete metrics for risk evaluation, and finally suggest specific mitigation strategies for developers and policymakers. As AI thought partners continue to proliferate, these strategies can help prevent major harms and ensure that humans actively benefit from productive thought partnerships.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

15 of 30 references.

References · 15

  1. 2024A Survey on LLM-as-a-Judgecited by 1,423
  2. 2024A Survey on Large Language Models for Code Generationcited by 658
  3. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  4. 2023Getting aligned on representational alignmentcited by 143
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  6. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
  7. 2024Building Machines that Learn and Think with Peoplecited by 73
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3 more
  1. 2014Invention as a Combinatorial Process: Evidence from U.S. Patentscited by 10
  2. 2024Infer Human's Intentions Before Following Natural Language Instructionscited by 10
  3. 2024When Should Algorithms Resign? A Proposal for AI Governancecited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.