Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-04-10 DOI:10.1111/insr.12492

Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen

引用次数: 6

Abstract

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.

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偏见，公平和问责与人工智能和机器学习算法

人工智能(AI)和机器学习算法的出现给它们的使用带来了机遇和挑战。在这篇概述文章中，我们首先讨论了使用人工智能技术时出现的偏见和公平问题，重点是监督机器学习算法。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本质。此外，我们对文献中的公平性指标进行了回顾，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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.