Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains
This provides a comprehensive testbed for advancing equitable and reliable AI systems, though it is incremental as it builds on existing fairness and domain adaptation research.
The authors tackled the challenge of fairness and robustness in machine learning under domain shifts by introducing Face4FairShifts, a large-scale facial image benchmark with 100,000 images across four domains and 39 annotations, which revealed significant performance gaps and limitations in existing datasets.
Ensuring fairness and robustness in machine learning models remains a challenge, particularly under domain shifts. We present Face4FairShifts, a large-scale facial image benchmark designed to systematically evaluate fairness-aware learning and domain generalization. The dataset includes 100,000 images across four visually distinct domains with 39 annotations within 14 attributes covering demographic and facial features. Through extensive experiments, we analyze model performance under distribution shifts and identify significant gaps. Our findings emphasize the limitations of existing related datasets and the need for more effective fairness-aware domain adaptation techniques. Face4FairShifts provides a comprehensive testbed for advancing equitable and reliable AI systems. The dataset is available online at https://meviuslab.github.io/Face4FairShifts/.