The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms. We describe the types and sources of data bias, and discuss the nature of algorithmic unfairness. This is followed by a review of fairness metrics in the literature, discussion of their limitations, and a description of de-biasing (or mitigation) techniques in the model life cycle.

翻译：AI 和 ML 算法的出现既带来了机遇,也带来了挑战。在本文件中,我们概述了使用 ML 算法产生的偏向和公平问题。我们描述了数据偏向的类型和来源,并讨论了算法不公平的性质。随后审查了文献中的公平度量,讨论了其局限性,并描述了模型生命周期中的偏向(或缓解)技术。