The Allure of Simplicity
On Interpretable Machine Learning Models in Healthcare
DOI:
https://doi.org/10.5195/pom.2023.139Keywords:
Explainable AI, Reliabilism, Machine learning, Medicine, Interpretability, OpacityAbstract
This paper develops an account of the opacity problem in medical machine learning (ML). Guided by pragmatist assumptions, I argue that opacity in ML models is problematic insofar as it potentially undermines the achievement of two key purposes: ensuring generalizability and optimizing clinician–machine decision-making. Three opacity amelioration strategies are examined, with explainable artificial intelligence (XAI) as the predominant approach, challenged by two revisionary strategies in the form of reliabilism and the interpretability by design. Comparing the three strategies, I argue that interpretability by design is most promising to overcome opacity in medical ML. Looking beyond the individual opacity amelioration strategies, the paper also contributes to a deeper understanding of the problem space and the solution space regarding opacity in medical ML.
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Deutsche Forschungsgemeinschaft
Grant numbers (BE5601/4-1; Cluster of Excellence “Machine Learning—New Perspectives for Science”, EXC 2064, project number 390727645). -
Carl-Zeiss-Stiftung
Grant numbers project "Certification and Foundations of Safe Machine Learning Systems in Healthcare" -
Baden-Württemberg Stiftung
Grant numbers AITE;(Artificial Intelligence, Trustworthiness and Explainability)
