arXiv:2605.01048v2 Announce Type: replace-cross
Abstract: Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and C...
By Zihao Yang, Mosh Levy, Yoav Goldberg, Byron C. Wallace
The study evaluates how clinical language models perform when patient narratives are presented in different voices—clinical, socioeconomic, and cultural—using 1,000 MedQA cases paired with three retellings each. All 12 tested models show a significant drop in accuracy (about 26% more errors) when the patient speaks in a marked voice, a penalty that persists even when the original vignette is included verbatim and is not explained by measured retelling properties. The authors find that rewriting the patient’s account into a clinical vignette before answering can halve this accuracy gap, though structured schema pipelines and additional reasoning tools offer limited improvements.
By Prabhjot Singh, Pritam Deka, Vijay Chennareddy
arXiv:2508. 08337v3 Announce Type: replace-cross Abstract: Algorithmic fairness research has largely framed unfairness as discrimination along sensitive attributes.
By Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sarah Stewart de Ramirez, Peter Spirtes, Kun Zhang, Sanmi Koyejo
arXiv:2609.22090v1 Announce Type: new
Abstract: An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present Ps...
By Joy Bose
arXiv:2608. 15254v1 Announce Type: new Abstract: Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI).
By Diego Mardian, Frank Liu
arXiv:2607. 27304v1 Announce Type: new Abstract: Medical vision-language models (VLMs) generate chain-of-thought (CoT) reasoning before answering clinical questions, but whether this reasoning causally influences predictions remains unclear.
By Supratik Bhowal, Subhrajyoti Basu, Aritra Gir Mahanta, Anik Pal Chowdhury