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
The study audits eight text-to-image models (four Western, four Chinese) by generating 56,000 synthetic faces under neutral and six emotion prompts. It finds pervasive demographic biases: young and White-coded faces are overrepresented, while combinations like young female Black faces are nearly absent. Emotion prompts further skew outputs, with negative emotions favoring White, middle-aged, male-coded faces and lower perceived attractiveness. The research highlights the need for intersectional, emotion‑conditioned, multilingual audits before deploying such models.
By Mengting Wei, Aditya Gulati, Guoying Zhao, Nuria Oliver
arXiv:2608.24219v1 Announce Type: new
Abstract: Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with...
By Francisco M. L\'opez, Jochen Triesch
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
The study shows that when people enjoy an artwork, they are more likely to believe it was created by a human, regardless of whether it was actually AI-generated or not. In a large online Turing Test, 22.1 percentage points more of respondents who favored a picture thought it was human-made, and this pattern held across all images and detection methods. Even those who were skeptical of AI art often preferred AI-generated pieces but still attributed them to humans, indicating a strong link between aesthetic preference and perceived authorship.
By Zihan Chen, Di Zhu, Lei Nico Zheng
arXiv:2605. 20731v2 Announce Type: replace-cross Abstract: Text-to-image models now generate graphic design at production scale, yet their supervision still comes primarily from photo-style preference datasets with a single overall verdict per comparison.
By Haonan Zhu, Elad Hirsch, Alexandria Minetti, Allison Nulty, Purvanshi Mehta
This paper examines how two popular vision‑language models encode protected identities when asked to transform human face images to reflect race and sexual orientation. The authors find that the models often comply with such requests, with compliance being non‑deterministic and group‑sensitive, and that the generated images are statistically distinguishable and tend to reinforce deep‑seated stereotypes. They also show that these representation biases can reappear in textual reasoning tasks and that image generation can perpetuate bias around criminality, raising concerns about AI‑assisted profiling.
By Soumyajit Datta, Arka Dutta, Ashiqur R. KhudaBukhsh
arXiv:2510.08831v2 Announce Type: replace
Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standar...
By Wouter Haverals, Meredith Martin
The study audits four image aesthetic scorers—LAION-Aesthetics, PickScore, ImageReward, and HPSv2—using pixel‑level interventions on skin tone and body type in both synthetic and real images. It finds that most scorers exhibit a fidelity preference: unaltered images receive the highest scores, while perturbations in either direction are penalized in an inverted‑U pattern, and this effect is largely independent of the skin operator. Synthetic‑only audits are misleading, as the apparent preference for darker skin in synthetic faces reverses or weakens when evaluated on real faces, and cross‑scorer results vary widely, underscoring the need for real‑data, within‑image causal isolation to accurately assess demographic bias.
By Mingyang Xu
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
By Maxime Moussi, Beno\^it Ronval, Siegfried Nijssen, F\'elicien Schiltz
arXiv:2610.27516v1 Announce Type: cross
Abstract: Despite advances in photorealism, semantic fidelity, and aesthetics, text-to-image generation and image editing remain understudied in a human-center...
By Junjie Xu, Yuzhe Yang, Rui Yang, Luwei Xiao, Wenxin Wu, Xingjiao Wu, Liang He