Towards Fair Medical Risk Prediction Software

Wolfram Luther and Ekaterina Auer ()
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Wolfram Luther: Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, 47057 Duisburg, Germany
Ekaterina Auer: Department of Electrical Engineering and Computer Science, University of Applied Sciences Wismar, Philipp-Mueller-Straße 14, 23966 Wismar, Germany

Future Internet, 2025, vol. 17, issue 11, 1-38

Abstract: This article examines the role of fairness in software across diverse application contexts, with a particular emphasis on healthcare, and introduces the concept of algorithmic (individual) meta-fairness. We argue that attaining a high degree of fairness—under any interpretation of its meaning—necessitates higher-level consideration. We analyze the factors that may guide the choice of a fairness definition or bias metric depending on the context, and we propose a framework that additionally highlights quality criteria such as accountability, accuracy, and explainability, as these play a crucial role from the perspective of individual fairness. A detailed analysis of requirements and applications in healthcare forms the basis for the development of this framework. The framework is illustrated through two examples: (i) a specific application to a predictive model for reliable lower bounds of BRCA1/2 mutation probabilities using Dempster–Shafer theory, and (ii) a more conceptual application to digital, feature-oriented healthcare twins, with the focus on bias in communication and collaboration. Throughout the article, we present a curated selection of the relevant literature at the intersection of ethics, medicine, and modern digital society.

Keywords: fairness; bias; meta-fairness; risk prediction; healthcare; digital twins; quality criteria (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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