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AI models show racial bias in trusting information, study finds

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper reveals that leading language models from OpenAI and Anthropic exhibit a probability bias, refusing to trust information from reporters described as Black more often than those described as White. This bias is subtle, appearing when models assess reporters based on race rather than evidence, and it worsens with increased reasoning effort. The study found that models invoked race for English terms like "Black" but not for their Spanish translation "negro," and never for "Indigenous." However, when information was assessed, the Black-described reporter received slightly more trust. A proposed scoring option that rewards models for accurately assessing information over repeated rounds significantly reduced this racial gap in trust. AI

IMPACT Reveals subtle racial biases in LLMs that may impact fairness and trust in AI systems.

RANK_REASON Research paper published on arXiv detailing bias in language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models show racial bias in trusting information, study finds

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Research paper published on arXiv detailing bias in language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Christian Arnold, Ernesto Calvo, Guadalupe Gonzalez, Sebasti\'an Vallejo Vera, Tiago Ventura ·

    Invisibilization as a Source of Probability Bias in Language Models

    arXiv:2610.30785v1 Announce Type: new Abstract: Language models are aligned to avoid using protected attributes such as race, ethnicity, and gender to inform their responses. Therefore, they increasingly refuse or hedge when such attributes appear in a prompt. However, if a syste…

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