arXiv AI July 9, 2026 By Sangwon Jung, Sumin Yu, Sanghyuk Chun, Taesup Moon

Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study

arXiv:2607. 06603v1 Announce Type: cross Abstract: The notion of algorithmic fairness has been actively explored from various aspects of fairness, such as counterfactual fairness (CF) and group fairness (GF).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 1

WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation

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

rag benchmarks safety

Hugging Face Trending Papers

Aug 10

CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis

Fairness evaluation in computer vision commonly relies on aggregate accuracy and demographic subgroup analysis. However, visual models are also sensitive to contextual factors such as illumination, blur, image quality, facial accessories, and appearance attributes.

computer-vision safety

arXiv Machine Learning

Sep 3

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

FairLens is a benchmark and evaluation framework that measures fairness and validity of vision‑language models (VLMs) in high‑stakes domains such as hiring, legal, and healthcare. It uses over 100,000 face‑image and question pairs covering gender, race, and age, and assesses responses through demographic parity, soundness, demographic association, and bias in free‑text generation. The study finds that VLMs often make unwarranted inferences from faces rather than abstaining, especially in legal and healthcare contexts, and that small parity gaps can still hide unsafe treatment across groups.

By Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza

llms multimodal benchmarks safety

arXiv Computer Vision

Sep 1

FairReL: Deepfake Detection using Fairness-Aware Representation Learning

FairReL is a fairness‑aware representation‑learning framework for deepfake detection that targets two subgroup‑sensitive components: multi‑scale spatial features and fine‑tuning‑induced residual features. It uses an SVD‑decomposed backbone to isolate residuals and introduces Group‑Conditional Wavelet Decorrelation (GCWD) and Subspace‑Localised Mean Alignment (SLMA) losses to suppress subgroup imbalance and align subgroup means. Experiments on FF++, Celeb‑DF, DFD, and DFDC show that FairReL improves unseen‑dataset AUC by 3.9% and reduces subgroup FPR disparity by 10.2% compared to the state‑of‑the‑art fairness‑aware detector.

By Xiaoman Lu, Jiaqi Li, Shuntian Zheng, Huiping Chen, Yu Guan

fine-tuning benchmarks safety

arXiv Computer Vision

Aug 25

Fairness-Aware Low-Rank Representation Fine-Tuning

arXiv:2503.05684v2 Announce Type: replace-cross Abstract: Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of...

By Parameswaran Kamalaruban, Mark Anderson, Stuart Burrell, Maeve Madigan, Piotr Skalski, David Sutton

fine-tuning safety

arXiv AI

Jun 2

Fair Finetuning Mitigates Distribution Inference Attacks

arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).

By Rakshit Naidu

nlp fine-tuning safety