arXiv Machine Learning

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.

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
arXiv Computer Vision
Sep 16

DenseFace: Bias Mitigation in Face Recognition via Density-Aware Probabilistic Matching

DenseFace introduces a bias‑mitigation technique for face recognition that operates on pre‑trained models without retraining. It models each person’s face embeddings with a von Mises‑Fisher distribution and uses a density‑aware probabilistic matching procedure to account for demographic differences. Experiments show that DenseFace consistently reduces racial bias across various architectures and datasets while preserving recognition accuracy.

By Mansur Bultygov, Vadim Seliutin, Dmitry Nekhaev, Ivan Laptev
arXiv Computer Vision
Aug 27

Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation

The paper introduces Semantic Boundary Predictor (SBP), an inference‑time framework that improves demographic fairness in synthetic face generation by applying a single, one‑shot intervention during reverse denoising. SBP learns linear semantic boundaries from late‑stage latent representations and applies them only at the initial noisy latent, leaving the rest of the diffusion process unchanged. Experiments on CelebA‑HQ show significant reductions in fairness disparity—98% for gender, 95% for binary race, and 15% for four‑class race—while preserving image quality across demographic groups.

By Subir Kumar Parida, Rajbabu Velmurugan, Ketan Kotwal, R. S. Sengar, Swati Hiremath
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