  Does everyone equally benefit from computer vision systems? Answers to this
question become more and more important as computer vision systems are deployed
at large scale, and can spark major concerns when they exhibit vast performance
discrepancies between people from various demographic and social backgrounds.
Systematic diagnosis of fairness, harms, and biases of computer vision systems
is an important step towards building socially responsible systems. To initiate
an effort towards standardized fairness audits, we propose three fairness
indicators, which aim at quantifying harms and biases of visual systems. Our
indicators use existing publicly available datasets collected for fairness
evaluations, and focus on three main types of harms and bias identified in the
literature, namely harmful label associations, disparity in learned
representations of social and demographic traits, and biased performance on
geographically diverse images from across the world.We define precise
experimental protocols applicable to a wide range of computer vision models.
These indicators are part of an ever-evolving suite of fairness probes and are
not intended to be a substitute for a thorough analysis of the broader impact
of the new computer vision technologies. Yet, we believe it is a necessary
first step towards (1) facilitating the widespread adoption and mandate of the
fairness assessments in computer vision research, and (2) tracking progress
towards building socially responsible models. To study the practical
effectiveness and broad applicability of our proposed indicators to any visual
system, we apply them to off-the-shelf models built using widely adopted model
training paradigms which vary in their ability to whether they can predict
labels on a given image or only produce the embeddings. We also systematically
study the effect of data domain and model size.
