Mitigating Discontinuance in Medical AI Systems: The Role of AI Explanations

Aycan Aslan, Maike Greve, Lutz M. Kolbe

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

Despite significant advancements in medical artificial intelligence (AI) systems, these technologies are prone to mistake in their predictions. These mistakes can significantly affect medical experts’ willingness to continue using these systems. To mitigate potential discontinuation, existing research indicates that providing additional information alongside predictions, can lessen negative out- comes like discontinuation. Given the potential impact on users’ information processing, we hypothesize that AI explanations, detailing the system's decision- making process, can also influence the likelihood of discontinuing use after an AI mistake. Through an online experiment with medical experts (n=227), we demonstrate that such explanations can influence medical experts’ information processing and, consequently, mitigate the adverse effects on the actual discontinuation of AI systems following a mistake.
Original languageEnglish
Publication date2024
Number of pages15
Publication statusPublished - 2024
Externally publishedYes
Event19th International Conference on Wirtschaftsinformatik. WI 2024 - Universität Würzburg, Würzburg, Germany
Duration: 16 Sept 202419 Sept 2024
Conference number: 17
https://wi2024.de/

Conference

Conference19th International Conference on Wirtschaftsinformatik. WI 2024
Number17
LocationUniversität Würzburg
Country/TerritoryGermany
CityWürzburg
Period16/09/202419/09/2024
Internet address

Keywords

  • Artificial intelligence
  • Decision-making
  • Explainability
  • Discontinuance
  • Medicine

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