The paper investigates how large language models (LLMs) can exhibit stigma toward people with psychological conditions by examining their intermediate reasoning steps rather than just final answers. Using clinical expertise, the authors develop a framework to identify and rate stigmatizing language in LLM reasoning, distinguishing between overt prejudice and subtler biases. They also expand an existing mental health stigma benchmark to include more psychological conditions, finding that reasoning analysis reveals far more stigma than traditional multiple-choice evaluations and exposes flaws in the models’ logic and understanding of mental health.
By Sreehari Sankar, Aliakbar Nafar, Mona Barman, Hannah K. Heitz, Ashwin Kumar, Pouria Tohidi, Dailun Li, Danish Hussain, Russell DuBois, Hamed Hasheminia, Farshad Majzoubi
The study introduces GAPA, a dataset of 316 physical attributes with 14,706 gender-association ratings from 304 US annotators, showing that such descriptions carry structured gender associations. It evaluates 16 LLMs, finding they partially mirror human ratings but exhibit biases such as compressed distributions, weaker alignment for men, and asymmetric abstention toward non‑binary identities. A proxy model trained on these data is released and applied to analyze character descriptions in LitBank, illustrating the persistence of gendered interpretations in ostensibly neutral language.
By Yingjia Wan, Lin Lin, Elisa Kreiss
arXiv:2609.16366v1 Announce Type: cross
Abstract: When foundation models describe people, recent work in AI fairness, accessibility, and ethics recommends avoiding inferred identity labels (e.g., "sh...
By Yingjia Wan, Lin Lin, Elisa Kreiss
arXiv:2607. 26062v1 Announce Type: cross Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID).
By Karly V. Coffey, Gloria L. Krahn, John P. Hanley, Jacob E. Neely
arXiv:2609.01548v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma's profound e...
By Stephanie Fong, Yiwen Jiang, Zimu Wang, Hongxi Yang, Yaling Shen, Hiu Weh Naomi Chow, Heung Ying Lai, Xiangyu Zhao, Qingyang Xu, Zhongxing Xu, Jiahe Liu, Guilherme C. Oliveira, Vincent Lee, Zongyuan Ge, Dominic Dwyer
arXiv:2601.13235v2 Announce Type: replace-cross
Abstract: Caregivers seeking AI-mediated support express complex needs---information-seeking, emotional validation, and distress cues---that warrant ca...
By Drishti Goel, Jeongah Lee, Qiuyue Joy Zhong, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Dong Whi Yoo, Koustuv Saha
The paper presents a new benchmark for automatically evaluating mental health stigma in online text, featuring a fine‑grained taxonomy that covers stigma mode, domain, and specific components across multiple mental health conditions. The authors annotate naturally occurring news and social media posts and test large language models and classifiers for sentiment, toxicity, and hate speech, finding that these models poorly capture stigma and often overpredict it without explicit rules. The benchmark, annotations, exemplar cases, and code are publicly released on GitHub.
By Naomi Baes, Jemima Kang, Nick Haslam, Chris Groot, Alsa Wu, Luc Raszewski, Yulia Otmakhova
arXiv:2602. 05088v4 Announce Type: replace Abstract: Millions of people now use generative AI chatbots for psychological support.
By Kate H. Bentley, Luca Belli, Adam M. Chekroud, Emily J. Ward, Emily R. Dworkin, Emily Van Ark, Kelly M. Johnston, Will Alexander, Millard Brown, Matt Hawrilenko
The study investigates how large language models (LLMs) assess psychological distress in online posts from six identity‑based communities. Through a perspectivist annotation task, 321 participants provided 9,587 judgments on 1,198 Reddit posts, revealing modest in‑group agreement (OR = 1.18) that varies across communities. When evaluated against these community‑specific labels, open‑weight LLMs consistently over‑estimate distress—achieving only 31–44% accuracy on posts perceived as none‑to‑mild—while newer models like GPT‑5 and Gemini 2.5 Pro show similar inflation, whereas Claude Opus 4 is more conservative.
"whyItMatters":"The findings highlight that miscalibrated distress detection by LLMs can disproportionately impact the very communities they aim to serve, underscoring the need for equitable AI deployment in mental‑health contexts."
By Andrew Aquilina, Xiang Lorraine Li, Yu-Ru Li
arXiv:2606. 28334v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) and social media data have led to growing optimism about the ability to detect suicide risk at scale.
By Yaakov Ophir, Ofri Hefetz, Refael Tikochinski, Kfir Bar, Shir Lissak, Shulamit Grinapol, Haya Wachtel, Eyal Fruchter, Roi Reichart
arXiv:2511.11689v4 Announce Type: replace-cross
Abstract: Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-a...
By Thomas D. Hull, Lizhe Zhang, Caitlin A. Stamatis, Patricia A. Arean, Matteo Malgaroli
arXiv:2602.11391v5 Announce Type: replace
Abstract: Objective: This study develops and validates a patient simulation framework that aligns with the National Institute of Standards and Technology AI...
By Md Tanvir Rouf Shawon, Mohammad Sabik Irbaz, Hadeel R. A. Elyazori, Keerti Reddy Resapu, Yili Lin, Vladimir Franzuela Cardenas, K. Pierre Eklou, Farrokh Alemi, Kevin Lybarger