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

Summary 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...

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Bibliographic Details
Published inInternational statistical review Vol. 90; no. 3; pp. 468 - 480
Main Authors Zhou, Nengfeng, Zhang, Zach, Nair, Vijayan N., Singhal, Harsh, Chen, Jie
Format Journal Article
LanguageEnglish
Published Hoboken John Wiley & Sons, Inc 01.12.2022
Subjects
algorithm
Algorithms
Artificial intelligence
Bias
fairness
Machine learning
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Summary:Summary 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.
Bibliography:The views expressed in the paper are those of the authors and do not represent the views of Wells Fargo.
ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12492