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

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

International Statistical Review, 2022, vol. 90, issue 3, 468-480

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bla:istatr:v:90:y:2022:i:3:p:468-480

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