The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic unfairness. In addition, we provide a review of fairness metrics in the literature, discuss their limitations, and describe de-biasing (or mitigation) techniques in the model life cycle.

中文翻译：

---

人工智能和机器学习算法的偏见、公平和问责

人工智能 (AI) 和机器学习算法的出现为它们的使用带来了机遇和挑战。在这篇概述论文中，我们首先讨论了使用 AI 技术引起的偏见和公平问题，重点是有监督的机器学习算法。然后，我们描述了数据偏差的类型和来源，并讨论了算法不公平的性质。此外，我们对文献中的公平指标进行了回顾，讨论了它们的局限性，并描述了模型生命周期中的去偏（或缓解）技术。