As computer vision systems are being increasingly deployed at scale in high-stakes applications like autonomous driving, concerns about social bias in these systems are rising. Analysis of fairness in real-world vision systems, such as object detection in driving scenes, has been limited to observing predictive inequity across attributes such as pedestrian skin tone, and lacks a consistent methodology to disentangle the role of confounding variables e.g. does my model perform worse for a certain skin tone, or are such scenes in my dataset more challenging due to occlusion and crowds? In this work, we introduce ICON2, a framework for robustly answering this question. ICON2 leverages prior knowledge on the deficiencies of object detection systems to identify performance discrepancies across sub-populations, compute correlations between these potential confounders and a given sensitive attribute, and control for the most likely confounders to obtain a more reliable estimate of model bias. Using our approach, we conduct an in-depth study on the performance of object detection with respect to income from the BDD100K driving dataset, revealing useful insights.
@article{arxiv.2306.04482,
title = {ICON$^2$: Reliably Benchmarking Predictive Inequity in Object Detection},
author = {Sruthi Sudhakar and Viraj Prabhu and Olga Russakovsky and Judy Hoffman},
journal= {arXiv preprint arXiv:2306.04482},
year = {2023}
}