{"slug": "toward-a-social-psychology-of-ai", "title": "Toward a Social Psychology of AI", "summary": "A preprint submitted to arXiv on 29 Jul 2026 reports that language-model agents reproduce human-like minimal-group bias, showing in-group favouritism when distributing points among anonymous peers with arbitrary group labels. Across four reasoning models, minority deciders over-allocated to their own group, majority deciders allocated nearly proportionally, and the asymmetry disappeared at equal group sizes; disabling reasoning in one model did not remove the disposition but nearly erased the minority-majority asymmetry. The authors propose social psychology's methods as a paradigm for measuring and governing AI's social behaviour.", "body_md": "# Physics > Physics and Society\n\n  [Submitted on 29 Jul 2026]\n\n# Title:Toward a social psychology of AI: language-model agents reproduce human-like minimal-group bias\n\n[View PDF](/pdf/2609.00009)\n\n[HTML (experimental)](https://arxiv.org/html/2609.00009v1)\n\nAbstract:Language-model agents now interact in groups, but evaluations that probe memorised stereotype content or use models to simulate people leave this social behaviour unmeasured. We adapt the minimal-group paradigm---social psychology's classic test of intergroup bias---into a controlled probe: an agent distributes points among anonymous peers bearing only an arbitrary group label. Across four reasoning models, mere categorisation into meaningless groups elicited in-group favouritism that vanished under a group-blind control and was concentrated in the numerical minority: minority deciders over-allocated to their own group relative to their numbers, majority deciders allocated close to proportionally, and the asymmetry closed at equal group sizes. Disabling reasoning in one model did not remove the disposition---if anything it grew---but nearly erased the minority-majority asymmetry, implicating deliberation in where bias concentrates rather than whether it appears. These open-weight reasoning models reproduce the behavioural signature of human intergroup discrimination, independent of stereotype content, and social psychology's theories and methods offer a paradigm for measuring and governing AI's social behaviour.\n    \n\n### Current browse context:\n\nphysics.soc-ph\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/toward-a-social-psychology-of-ai", "canonical_source": "https://arxiv.org/abs/2609.00009", "published_at": "2026-09-07 12:09:06+00:00", "updated_at": "2026-09-07 12:27:17.233755+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-ethics", "ai-safety"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/toward-a-social-psychology-of-ai", "markdown": "https://wpnews.pro/news/toward-a-social-psychology-of-ai.md", "text": "https://wpnews.pro/news/toward-a-social-psychology-of-ai.txt", "jsonld": "https://wpnews.pro/news/toward-a-social-psychology-of-ai.jsonld"}}