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 ICON$^2$, a framework for robustly answering this question. ICON$^2$ 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.

翻译：随着计算机视觉系统在自动驾驶等高风险应用中大规模部署，人们对这些系统中社会偏见的担忧日益增加。现实世界视觉系统（如驾驶场景中的目标检测）的公平性分析，目前仅限于观察诸如行人肤色等属性上的预测不公，且缺乏一致的方法来厘清混杂变量的作用——例如，我的模型对某种肤色表现更差，还是由于遮挡和人群拥挤导致此类场景在数据集中更具挑战性？在本文中，我们引入ICON²框架，用于稳健地回答这一问题。ICON²利用目标检测系统缺陷的先验知识，识别子群体间的性能差异，计算这些潜在混杂因素与给定敏感属性之间的相关性，并控制最可能的混杂因素，从而获得更可靠的模型偏差估计。通过我们的方法，我们对BDD100K驾驶数据集中目标检测性能与收入的关系进行了深入研究，揭示了有价值的见解。