The paper audits 27 text‑to‑image generators released between 2022 and 2026, examining 51,300 AI‑generated faces and 160,196 real photographs. Using automated metrics and 550 human raters, it finds that AI‑generated faces consistently score higher on attractiveness than real faces, with female‑prompted images often surpassing professional models and darker‑skinned groups receiving lower scores. A preregistered hiring simulation shows that exposure to AI‑generated attractive faces reduces hireability and trustworthiness ratings for real candidates, whereas retouched photographs have no such effect.
By AJ Alvero, Deepak Varuvel Dennison, Alexandra Crespin, Heeyon Kim, Alejandra Regla-Vargas, Avani Akerkar, Jasmine McNealy, Ren\'e Kizilcec
arXiv:2609.24228v1 Announce Type: new
Abstract: Text-to-image (T2I) models are typically evaluated for bias using slot-based templates such as ``a photo of a [profession]''. Such templates probe only...
By Yue Dai, Ziyang Liu, Marc Cheong, Caren Han
arXiv:2508. 03483v3 Announce Type: replace-cross Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored.
By Dasol Choi, Jihwan Lee, Minjae Lee, Minsuk Kahng
Beauty assessments from Multimodal Large Language Models (MLLMs) are increasingly popular, prompting a study comparing 2,513 human ratings to four commercial AI models—Claude, Gemini, GPT, and Grok. The study found that MLLMs consistently rate faces more favorably and with a narrower range than humans, yet they maintain strong correlations with human judgments and accurately track the rank‑ordering of faces. While all models agree strongly with each other, Grok showed the lowest agreement with human ratings, and only face age emerged as a common predictor of attractiveness across humans and MLLMs.
By Santiago Grandas, Juan Sebastian Cely-Acosta, Mohit Mendiratta, Shafee Hassan, Macken Murphy
The paper shows that in text‑to‑image evaluation, human preference for attractive faces can outweigh objective image quality, leading to biased rankings and model outputs. A user study revealed that annotators consistently rate more attractive faces as higher quality, even when comparing real photographs of equal fidelity. This bias can invert leaderboard rankings, dominate preference model scores, and cause generated faces to shift toward younger, more feminine features, undermining user control.
By Harvey Mannering, Zhiwu Huang, Adam Pr\"ugel-Bennett
arXiv:2610.33021v1 Announce Type: cross
Abstract: Large language models (LLMs) increasingly deliver information through personas, but it is unclear whether correcting the facts also makes their frami...
By Nouar AlDahoul, Myles Joshua Toledo Tan, Yasir Zaki
arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.
By Bhaskara Hanuma Vedula, Darshan Anghan, Ishita Goyal, Ponnurangam Kumaraguru, Abhijnan Chakraborty
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
By Sinie van der Ben, Rapha\"el Baur, Yannick Metz, Mennatallah El-Assady
The paper examines how large language models (LLMs) respond to different demographic cues—such as names—when users seek advice, focusing on race and gender in a U.S. context. It finds that using different cues for the same group leads to only partially overlapping changes in model responses, producing inconsistent conclusions about personalization and unstable bias metrics. The authors argue that LLMs react to linguistic signals tied to cues rather than to stable demographic categories, and they call for evaluations that use multiple cues and consider underlying mechanisms.
By Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra, Sharif Kazemi, Victor Orozco-Olvera, Ana Mar\'ia Mu\~noz Boudet, Lakshmi Subramanian, Samuel P. Fraiberger, Sharath Chandra Guntuku, Valentin Hofmann
arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.
By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis
arXiv:2501. 02211v3 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias generalizes across models, is stable under different inference settings, or depends on how group identity is signaled remains unstudied.
By Messi H. J. Lee
The paper audits five AffectNet‑pretrained facial affect models on child faces, revealing that all models perform worse on naturalistic child images and consistently confuse fear with surprise. The degradation is linked to specific expression morphologies—particularly open‑mouth faces—and to South‑Asian children, rather than skin tone. A linear probe shows that most of the gap lies in the classifier head, and fine‑tuning just this head on a small amount of child data improves performance by up to 28% on the largest child datasets.
By Tobias Hallmen, Robin-Nico Kampa, Elisabeth Andr\'e