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m NIST’s AI Risk Management Framework 1.0 and its 2024 Generative AI Profile provide practical guidance for governing and measuring bias mitigation in AI systems. |
m →Racial and ethnic discrimination: Change "www.timesofisrael.com" to "The Times of Israel" |
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Another study, published in August 2024, on [[Large language model]] investigates how language models perpetuate covert racism, particularly through dialect prejudice against speakers of African American English (AAE). It highlights that these models exhibit more negative stereotypes about AAE speakers than any recorded human biases, while their overt stereotypes are more positive. This discrepancy raises concerns about the potential harmful consequences of such biases in decision-making processes.<ref>Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature 633, 147–154 (2024). https://doi.org/10.1038/s41586-024-07856-5</ref>
A study published by the [[Anti-Defamation League]] in 2025 found that several major LLMs, including [[ChatGPT]], [[Llama (language model)|Llama]], [[Claude (language model)|Claude]], and [[Gemini (language model)|Gemini]] showed antisemitic bias.<ref>{{
A 2018 study found that commercial gender classification systems had significantly higher error rates for darker-skinned women, with error rates up to 34.7%, compared to near-perfect accuracy for lighter-skinned men.<ref>{{Cite conference |last1=Buolamwini |first1=J. |last2=Gebru |first2=T. |title=Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification |book-title=Proceedings of the 1st Conference on Fairness, Accountability and Transparency |pages=77–91 |year=2018 |url=https://proceedings.mlr.press/v81/buolamwini18a.html |access-date=April 30, 2025}}</ref>
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