{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:07:13Z","timestamp":1774444033609,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Despite their performance, large language models (LLMs) can inadvertently perpetuate biases found in the data they are trained on. By analyzing LLM responses to bias-eliciting headlines, we find that these models often mirror human biases. To address this, we explore crowd-based strategies for mitigating bias through response aggregation. We first demonstrate that simply averaging responses from multiple LLMs, intended to leverage the ``wisdom of the crowd\", can exacerbate existing biases due to the limited diversity within LLM crowds. In contrast, we show that locally weighted aggregation methods more effectively leverage the wisdom of the LLM crowd, achieving both bias mitigation and improved accuracy. Finally, recognizing the complementary strengths of LLMs (accuracy) and humans (diversity), we demonstrate that hybrid crowds containing both significantly enhance performance and further reduce biases across ethnic and gender-related contexts.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/37","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"321-329","source":"Crossref","is-referenced-by-count":1,"title":["Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds"],"prefix":"10.24963","author":[{"given":"Axel","family":"Abels","sequence":"first","affiliation":[{"name":"Machine Learning Group, Universit\u00e9 Libre de Bruxelles"},{"name":"AI Lab, Vrije Universiteit Brussel"},{"name":"FARI, AI for the Common-Good Institute, ULB-VUB"}]},{"given":"Tom","family":"Lenaerts","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Universit\u00e9 Libre de Bruxelles"},{"name":"AI Lab, Vrije Universiteit Brussel"},{"name":"FARI, AI for the Common-Good Institute, ULB-VUB"},{"name":"Center for Human-Compatible AI, UC Berkeley"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:32:39Z","timestamp":1758627159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/37"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/37","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}