  Computer vision systems have witnessed rapid progress over the past two
decades due to multiple advances in the field. As these systems are
increasingly being deployed in high-stakes real-world applications, there is a
dire need to ensure that they do not propagate or amplify any discriminatory
tendencies in historical or human-curated data or inadvertently learn biases
from spurious correlations. This paper presents a comprehensive survey on
fairness that summarizes and sheds light on ongoing trends and successes in the
context of computer vision. The topics we discuss include 1) The origin and
technical definitions of fairness drawn from the wider fair machine learning
literature and adjacent disciplines. 2) Work that sought to discover and
analyze biases in computer vision systems. 3) A summary of methods proposed to
mitigate bias in computer vision systems in recent years. 4) A comprehensive
summary of resources and datasets produced by researchers to measure, analyze,
and mitigate bias and enhance fairness. 5) Discussion of the field's success,
continuing trends in the context of multimodal foundation and generative
models, and gaps that still need to be addressed. The presented
characterization should help researchers understand the importance of
identifying and mitigating bias in computer vision and the state of the field
and identify potential directions for future research.
