Automation Bias in AI-Decision Support: Results from an Empirical Study
Kücking F, Hübner U, Przysucha M, Hannemann N, Kutza JO, Moelleken M, Erfurt-Berge C, Dissemond J, Babitsch B, Busch D (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: IOS Press BV
Book Volume: 317
Pages Range: 298-304
Conference Proceedings Title: Studies in Health Technology and Informatics
Event location: Dresden, DEU
ISBN: 9781643685366
DOI: 10.3233/SHTI240871
Abstract
Introduction Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention. This study therefore aims to identify factors influencing automation bias in a diagnostic task. Methods A quantitative intervention study with participants from different backgrounds (n = 210) was conducted, employing regression analysis to analyze potential factors. Automation bias was measured as the agreement rate with wrong AI-enabled recommendations. Results and Discussion Diagnostic performance, certified wound care training, physician profession, and female gender significantly reduced false agreement rates. Higher perceived benefit of the system was significantly associated with promoting false agreement. Strategies like comprehensive diagnostic training are pivotal in the prevention of automation bias when implementing CDSS. Conclusion Considering factors influencing automation bias when introducing a CDSS is critical to fully leverage the benefits of such a system. This study highlights that non-specialists, who stand to gain the most from CDSS, are also the most susceptible to automation bias, emphasizing the need for specialized training to mitigate this risk and ensure diagnostic accuracy and patient safety.
Authors with CRIS profile
Involved external institutions
Hochschule Osnabrück
Germany (DE)
Universität Osnabrück
Germany (DE)
Universitätsklinikum Essen
Germany (DE)
Germany (DE)
Universität Osnabrück
Germany (DE)
Universitätsklinikum Essen
Germany (DE)
How to cite
APA:
Kücking, F., Hübner, U., Przysucha, M., Hannemann, N., Kutza, J.O., Moelleken, M.,... Busch, D. (2024). Automation Bias in AI-Decision Support: Results from an Empirical Study. In Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf (Eds.), Studies in Health Technology and Informatics (pp. 298-304). Dresden, DEU: IOS Press BV.
MLA:
Kücking, Florian, et al. "Automation Bias in AI-Decision Support: Results from an Empirical Study." Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024, Dresden, DEU Ed. Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf, IOS Press BV, 2024. 298-304.
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