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.

本文概述了在使用人工智能算法时如何识别、测量和提高算法公正性的主要概念，并讨论了算法偏见和不公正的原因、公正的常见定义和度量以及预处理、处理中和后处理机制，最终回顾了几个新兴的算法公正性研究子领域。