{"type":"ScholarlyArticle","title":"FAIRGAME: A Framework for AI Agents Bias Recognition Using Game Theory (Extended abstract)","authors":[{"name":"Alessio Buscemi","slug":"alessio-buscemi","given":"Alessio","family":"Buscemi","affiliation":""},{"name":"Daniele Proverbio","slug":"daniele-proverbio","given":"Daniele","family":"Proverbio","affiliation":""},{"name":"Alessandro Di Stefano","slug":"alessandro-di-stefano","given":"Alessandro","family":"Di Stefano","affiliation":""},{"name":"The-Anh Han","slug":"the-anh-han","given":"The Anh","family":"Han","affiliation":""},{"name":"German Castignani","slug":"german-castignani","given":"German","family":"Castignani","affiliation":""},{"name":"Pietro Liò","slug":"pietro-lio","given":"Pietro","family":"Liò","affiliation":""}],"abstract":"Letting AI agents interact in multi-agent settings introduces significant complexity in predicting and interpreting their collective behavior, with profound implications for trustworthy AI adoption in research and society. We present FAIRGAME (Framework for AI Agents Bias Recognition using Game Theory), an open-source framework that simulates game-theoretic scenarios with LLM-based agents to systematically uncover biases arising from model choice, language, agent personality, and more. Applied to the Prisoner’s Dilemma and Battle of the Sexes across four LLMs and five human languages, FAIRGAME reveals inconsistencies across LLM models and consistent deviations from game-theoretic predictions; it also quantifies LLM-specific behavioral tendencies through a novel scoring system. Our results show that LLMs draw on prior world knowledge beyond payoff matrices, and that language and personality significantly shape strategic outcomes, supporting the use of reproducible and controlled simulation pipelines to predict the interacting behavior of LLM agents.","doi":"10.24963/ijcai.2026/919","url":"https://ijcai.org/proceedings/2026/919","pdf":"https://ijcai.org/proceedings/2026/0919.pdf","markdown":"https://ijcai.org/proceedings/2026/919.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/919","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":919,"pages":{"first":8237,"last":8242},"keywords":["Agent-based and Multi-agent Systems: Coordination and cooperation","Agent-based and Multi-agent Systems: Agent-based simulation and emergence","AI: AI Ethics, Trust, Fairnes","Agent-based and Multi-agent Systems: Engineering methods, platforms, languages and tools"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}