arXiv:2510. 21011v3 Announce Type: replace-cross Abstract: As generative AI tools are increasingly used to portray people in professional roles, understanding their racial and gender representational biases is critical.
By Ilona van der Linden, Sahana Kumar, Arnav Dixit, Aadi Sudan, Smruthi Danda, David C. Anastasiu, Kai Lukoff
arXiv:2605.31556v2 Announce Type: replace-cross
Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succe...
By Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji
The paper investigates how in‑context learning (ICL) in large vision‑language models (LVLMs) can amplify gender bias. Using the VL‑BICLE framework, the authors show that gendered ICL demonstrations shift model bias toward the demonstrated gender, especially in tasks involving gendered language such as image captioning and pronoun prediction. They find that similarity‑based retrieval does not mitigate this bias and that replacing real images with synthetic ones from stable diffusion reduces bias without hurting caption quality.
By Tong Xiang, Noa Garcia, Yuta Nakashima
The paper investigates how safety evaluations for large language models may mask ongoing gender discrimination by transforming harmful content rather than eliminating it, a phenomenon termed "harm laundering." Analyzing 450,000 gender‑directed completions across GPT‑2 to GPT‑5, the authors find that sexual violence content directed at women disappears while men receive more positive representations, with GPT‑5 showing stark disparities such as framing breast cancer as a men’s rights debate. The study introduces a formal test and detection protocol for harm laundering, demonstrating that reduced toxicity scores do not necessarily reflect reduced representational harm.
By Sarah Wyer, Sue Black, Noura Al Moubayed
The paper investigates how safety evaluations for large language models may mask ongoing gender discrimination, a phenomenon the authors term "harm laundering." By analyzing 450,000 gender‑directed completions across GPT‑2 to GPT‑5, they show that harmful content directed at women is transformed rather than removed, while men receive more positive representations. The study introduces a formal test and detection protocol for harm laundering, demonstrating that reduced toxicity scores do not necessarily indicate reduced representational harm.
arXiv:2608.30210v1 Announce Type: cross
Abstract: AI image generators now create face portraits that are hard to tell from real photographs. Vision-language models (VLMs) are increasingly proposed to...
By Sunwhi Kim (Hwasung Medi-Science University, Dept. of Bio-Healthcare), Sunyul Kim (Yonsei University, Graduate School of Engineering, Dept. of Artificial Intelligence), Meounggun Jo (Hoseo University), Jini Tae (Gwangju Institute of Science and Technology, School of Humanities and Social Sciences)