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Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms
International Statistical Review ( IF 1.7 ) Pub Date : 2022-04-10 , DOI: 10.1111/insr.12492
Nengfeng Zhou 1 , Zach Zhang 1 , Vijayan N. Nair 1 , Harsh Singhal 1 , Jie Chen 1
Affiliation  

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。
更新日期:2022-04-10
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