Distribution-aware Fairness Test Generation

arXiv:2305.13935v4 Announce Type: replace-cross
Abstract: Ensuring that all classes of objects are detected with equal accuracy is essential in AI systems. For instance, being unable to identify any one class of objects could have fatal consequences in autonomous driving systems. Hence, ensuring the reliability of image recognition systems is crucial. This work addresses how to validate group fairness in image recognition software. We propose a distribution-aware fairness testing approach (called DistroFair) that systematically exposes class-level fairness violations in image classifiers …

abstract accuracy ai systems arxiv autonomous autonomous driving autonomous driving systems class consequences cs.cv cs.lg cs.se distribution driving equal fairness identify image image recognition instance objects recognition reliability systems test type work