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Review
. 2024 Apr;115(4):687-697.
doi: 10.1002/cpt.3117. Epub 2023 Dec 12.

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science

Magnus Gray  1 , Ravi Samala  2 , Qi Liu  3 , Denny Skiles  4 , Joshua Xu  1 , Weida Tong  1 , Leihong Wu  1
Affiliations
Review

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science

Magnus Gray et al. Clin Pharmacol Ther. 2024 Apr.

Abstract

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision-making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern. Bias, as used in this paper, refers to the tendency toward a particular characteristic or behavior, and thus, a biased AI system is one that shows biased associations entities. In this literature review, we examine the current state of research on AI bias, including its sources, as well as the methods for measuring, benchmarking, and mitigating it. We also examine the biases and methods of mitigation specifically relevant to the healthcare field and offer a perspective on bias measurement and mitigation in regulatory science decision making.

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References

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    1. Oxford English Dictionary. bias, n., adj., adv. <https://www.oed.com/dictionary/bias_n> (2023).
    1. Managing Bias in AI. In Companion Proceedings of the 2019 World Wide Web Conference (eds. Roselli, D. et al.) (Association for Computing Machinery, New York, 2019).
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    1. Hovy, D. & Prabhumoye, S. Five sources of bias in natural language processing. Lang. Linguist. Compass 15, e12432 (2021).

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