  This paper summarizes and evaluates various approaches, methods, and
techniques for pursuing fairness in artificial intelligence (AI) systems. It
examines the merits and shortcomings of these measures and proposes practical
guidelines for defining, measuring, and preventing bias in AI. In particular,
it cautions against some of the simplistic, yet common, methods for evaluating
bias in AI systems, and offers more sophisticated and effective alternatives.
The paper also addresses widespread controversies and confusions in the field
by providing a common language among different stakeholders of high-impact AI
systems. It describes various trade-offs involving AI fairness, and provides
practical recommendations for balancing them. It offers techniques for
evaluating the costs and benefits of fairness targets, and defines the role of
human judgment in setting these targets. This paper provides discussions and
guidelines for AI practitioners, organization leaders, and policymakers, as
well as various links to additional materials for a more technical audience.
Numerous real-world examples are provided to clarify the concepts, challenges,
and recommendations from a practical perspective.
