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

被引：8

作者：

Zhou, Nengfeng ^{[1
]}

Zhang, Zach ^{[1
]}

Nair, Vijayan N. ^{[1
]}

Singhal, Harsh ^{[1
]}

Chen, Jie ^{[1
]}

机构：

[1] Wells Fargo, Corp Model Risk, San Francisco, CA 94114 USA

来源：

INTERNATIONAL STATISTICAL REVIEW | 2022年 / 90卷 / 03期

关键词：

AI; algorithm; bias; fairness; ML;

D O I：

10.1111/insr.12492

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：468 / 480

页数：13

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