# Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains

## TL;DR

Face4FairShifts introduces a large-scale facial image benchmark to evaluate fairness and robustness of models across diverse visual domains, highlighting current limitations and guiding future development of equitable AI systems.

## Contribution

The paper presents Face4FairShifts, a comprehensive dataset and benchmark for assessing fairness and domain generalization in facial image analysis.

## Key findings

- Existing datasets have significant limitations for fairness evaluation.
- Models show performance gaps under distribution shifts.
- The benchmark reveals the need for better fairness-aware domain adaptation methods.

## Abstract

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/.