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.

本文总结和评估了追求人工智能系统公正性的各种方法和技术，提出了定义、衡量和预防人工智能偏见的实用指南，并针对评估人工智能系统偏见的一些简单但常见方法提出了更为复杂和有效的替代方案。此外它还提供了一个共同的语言来解决领域内普遍存在的争议和混乱。它描述了涉及人工智能公正性的各种权衡，并提供了实用建议来平衡这些权衡。本文提供了关于人工智能从业者、组织领导、政策制定者的讨论和指南，以及为技术观众提供更多相关资料的链接。通过举例清晰阐述了本文中所提到的概念、挑战和建议。

AI公正：从原则到实践