Mitigating Bias in AI-supported Decision-making: Ophthalmologists Perceptions of Bias Mitigation Strategies in Detecting Diabetic Retinopathy

Nørgaard, Trine Munch ; Bach, Anne Kathrine Petersen ; Brok, Jens Christian

4. Term

Interaction Design, Master of Science (MSc)

2022

2022-05-31

27 pages

We are seeing an increase in the application of artificial intelligence (AI) into clinical decision support systems (CDSSs). Although this has the potential to alleviate clinicians' workload, the way AI output is presented to the users of AI-supported CDSSs potentially leads to cognitive biases that play a compromising role in the accuracy of diagnostic decisions. In this study, we investigate ophthalmologists' perceptions towards the implementation of three bias mitigation strategies into their current AI-supported CDSS workflow by designing an interactive prototype embodying these strategies. The prototype was evaluated through individual qualitative evaluations with six ophthalmologists to understand their view on the utility of the strategies in their current workflow. Our findings indicate that although the ophthalmologists saw potential in using the strategies to increase diagnostic accuracy, the necessity for efficiency, and the limited capabilities of the AI, rendered its use in practice unrealistic to some. Additionally, we found that varying task complexity levels had a substantial impact on the perceived usefulness of some of the strategies. Finally, the ophthalmologists perceived the strategies as precautionary measures, making some skeptical towards their use due to their self-perception as experts that outperform automated solutions.

diabetisk retinopati ; artificial intelligence ; bias mitigation ; clinical decision support system ; human-AI collaboration ; ophthalmology ; cognitive reasoning ; anchoring bias

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