arXiv Machine Learning
Jun 10
Toward Calibrated, Fair, and accurate Deepfake Detection
arXiv:2606. 09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups.
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.
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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.
arXiv AI
Jun 4
Adaptive Calibration for Fair and Performant Facial Recognition
arXiv:2606. 04469v1 Announce Type: cross Abstract: We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities.
arXiv AI
Jul 31
BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models
arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.
arXiv AI
Aug 3
DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
arXiv AI
Jul 9
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).
arXiv Machine Learning
Jul 17
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
arXiv:2607. 14705v1 Announce Type: new Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race.
arXiv AI
Jun 4
CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems
arXiv:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.
arXiv AI
Jul 17
Benchmarking Face Recognition without Real Faces
arXiv:2607. 14932v1 Announce Type: cross Abstract: Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs.
arXiv Machine Learning
Aug 10
Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs
arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.
Hugging Face Trending Papers
Jul 16
Benchmarking Face Recognition without Real Faces
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace.
arXiv Machine Learning
Jun 2
RAIGen: Rare Attribute Identification in Text-to-Image Generative Models
arXiv:2602. 06806v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes.