$\texttt {ModSCAN} $: Measuring Stereotypical Bias in Large Vision-Language Models from Vision...

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Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation

S Sun, L Liu, Y Liu, Z Liu, S Zhang, J Heikkilä… - arxiv preprint arxiv …, 2025 - arxiv.org
Bias in Foundation Models (FMs)-trained on vast datasets spanning societal and historical
knowledge-poses significant challenges for fairness and equity across fields such as …
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SB-Bench: Stereotype Bias Benchmark for Large Multimodal Models

V Narnaware, A Vayani, R Gupta, S Sirnam… - arxiv preprint arxiv …, 2025 - arxiv.org
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal
prejudices, undermining the fairness and equity of AI applications. As LMMs grow …
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Explainability for Vision Foundation Models: A Survey

R Kazmierczak, E Berthier, G Frehse… - arxiv preprint arxiv …, 2025 - arxiv.org
As artificial intelligence systems become increasingly integrated into daily life, the field of
explainability has gained significant attention. This trend is particularly driven by the …
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