Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships
Open Access
- 10 June 2026
- journal article
- Published by Association for Computing Machinery (ACM) in ACM AI Letters
- Vol. 1 (2) , 1-6
Abstract
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 characterize 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.Keywords
All Related Versions
Funding Information
- King's College Cambridge, University of Cambridge (EP/V025279/1)
- Leverhulme Trust
This publication has 20 references indexed in Scilit:
- A survey on LLM-as-a-judgeThe Innovation, 2026
- LLM-generated messages can persuade humans on policy issuesNature Communications, 2025
- Modern maxims for an AI oracleNature Machine Intelligence, 2025
- Building machines that learn and think with peopleNature Human Behaviour, 2024
- Dimensions of disagreement: Divergence and misalignment in cognitive science and artificial intelligence.Decision, 2024
- Learning by thinking in natural and artificial mindsTrends in Cognitive Sciences, 2024
- Evaluating language models for mathematics through interactionsProceedings of the National Academy of Sciences, 2024
- Problem choice and decision trees in science and engineeringCell, 2024
- Artificial intelligence and illusions of understanding in scientific researchNature, 2024
- Quantifying collective intelligence in human groupsProceedings of the National Academy of Sciences, 2021