Generates 550 roles and 33,000 questions to evaluate 10 LLMs in role-playing, finding 107,580 biased responses.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
Audit of 444 AI-generated occupational images finds women underrepresented in senior and tech roles, Black individuals nearly absent, people with visible disabilities completely absent, and younger people overrepresented.
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
citing papers explorer
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Fairness Testing of Large Language Models in Role-Playing
Generates 550 roles and 33,000 questions to evaluate 10 LLMs in role-playing, finding 107,580 biased responses.
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Generative AI Carries Non-Democratic Biases and Stereotypes: Representation of Women, Black Individuals, Age Groups, and People with Disability in AI-Generated Images across Occupations
Audit of 444 AI-generated occupational images finds women underrepresented in senior and tech roles, Black individuals nearly absent, people with visible disabilities completely absent, and younger people overrepresented.
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Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.