PhilSci Archive

The Allure of Simplicity: On Interpretable Machine Learning Models in Healthcare

Grote, Thomas (2023) The Allure of Simplicity: On Interpretable Machine Learning Models in Healthcare. Philosophy of Medicine, 4 (1). pp. 1-24. ISSN 2692-3963

Grote-Final.pdf - Published Version
Available under License Creative Commons Attribution.

Download (401kB) | Preview


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.

Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Published Article or Volume
Keywords: Explainable AI Reliabilism Machine learning Medicine Interpretability Opacity
Subjects: Specific Sciences > Medicine
Depositing User: Professor Alex Broadbent
Date Deposited: 14 Dec 2023 00:35
Last Modified: 14 Dec 2023 00:35
Item ID: 22843
Journal or Publication Title: Philosophy of Medicine
Publisher: University Library System, University of Pittsburgh
Official URL:
DOI or Unique Handle: 10.5195/pom.2023.139
Subjects: Specific Sciences > Medicine
Date: 27 September 2023
Page Range: pp. 1-24
Volume: 4
Number: 1
ISSN: 2692-3963

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item