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This paper conducts fairness testing on automated pedestrian detection, acrucial but under-explored issue in autonomous driving systems. We evaluateeight widely-studied pedestrian detectors across demographic groups onlarge-scale real-world datasets. To enable thorough fairness testing, weprovide extensive annotations for the datasets, resulting in 8,311 images with16,070 gender labels, 20,115 age labels, and 3,513 skin tone labels. Ourfindings reveal significant fairness issues related to age and skin tone. Thedetection accuracy for adults is 19.67% higher compared to children, and thereis a 7.52% accuracy disparity between light-skin and dark-skin individuals.Gender, however, shows only a 1.1% difference in detection accuracy.Additionally, we investigate common scenarios explored in the literature onautonomous driving testing, and find that the bias towards dark-skinpedestrians increases significantly under scenarios of low contrast and lowbrightness. We publicly release the code, data, and results to support futureresearch on fairness in autonomous driving.

Dark-Skin Individuals Are at More Risk on the Street: Unmasking Fairness Issues of Autonomous Driving Systems