The Challenge of Bias Mitigation in Clinical AI Decision Support: A Balance Between Decision Efficiency and Quality

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Abstract

An increasing number of intelligent data-driven health systems seek to support patients and clinicians in decision making tasks. However, the recommendations provided by such systems can negatively impact the reasoning abilities of its users, giving rise to cognitive biases. Such mental processes can subsequently harm the quality of the user's decision. While decision support systems are typically designed to increase user efficiency, known approaches to mitigate such biases primarily rely on slowing down the decision making process---offsetting any efficiency benefits. This position paper calls attention to the efficiency--quality trade-off in bias mitigation and outlines a future research direction for bias mitigation in AI decision support.
OriginalsprogEngelsk
TitelAdjunct Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems
Antal sider2
Publikationsdato2023
Sider1-2
StatusUdgivet - 2023
Begivenhed2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23 - Hamburg, Tyskland
Varighed: 23 apr. 202328 apr. 2023

Konference

Konference2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23
Land/OmrådeTyskland
ByHamburg
Periode23/04/202328/04/2023
NavnExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23)

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