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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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.
Original languageEnglish
Title of host publicationAdjunct Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems
Number of pages2
PublisherAssociation for Computing Machinery
Publication date2023
Pages1-2
Publication statusPublished - 2023
Event2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23 -
Duration: 23 Apr 202328 Apr 2023

Conference

Conference2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23
Period23/04/202328/04/2023
SeriesExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23)

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