ICON$^2$: Reliably Benchmarking Predictive Inequity in Object Detection
Sruthi Sudhakar Viraj Prabhu Olga Russakovsky Judy Hoffman
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.