论文标题
算法公平
Algorithmic Fairness
论文作者
论文摘要
关于人类日常生活的越来越多的决定受到人工智能（AI）算法的控制，从医疗保健，运输和教育到大学招生，招聘，提供贷款和更多领域。由于他们现在涉及我们生活的许多方面，因此开发不仅准确，而且客观和公平的AI算法至关重要。最近的研究表明，即使没有意图，算法的决策也可能天生就容易发生不公平。本文概述了使用AI算法时识别，测量和改善算法公平性的主要概念。本文首先讨论算法偏见和不公平的原因以及公平的共同定义和措施。然后，审查增强公平的机制并将其分为预处理，进程和后处理机制。然后对机制进行了全面的比较，以更好地理解在不同情况下应使用哪些机制。然后，本文描述了该字段中最常用的与公平相关的数据集。最后，本文以审查了算法公平的几个新兴研究子场的结尾。
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
