{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:10:36Z","timestamp":1781269836016,"version":"3.54.1"},"reference-count":90,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2014under any interpretation of its meaning\u2014necessitates 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\u2013Shafer 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.<\/jats:p>","DOI":"10.3390\/fi17110491","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T04:26:29Z","timestamp":1761711989000},"page":"491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Fair Medical Risk Prediction Software"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1245-7628","authenticated-orcid":false,"given":"Wolfram","family":"Luther","sequence":"first","affiliation":[{"name":"Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, 47057 Duisburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4059-3982","authenticated-orcid":false,"given":"Ekaterina","family":"Auer","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Applied Sciences Wismar, Philipp-Mueller-Stra\u00dfe 14, 23966 Wismar, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s41746-020-0304-9","article-title":"Predictably unequal: Understanding and addressing concerns that algorithmic clinical prediction may increase health disparities","volume":"3","author":"Paulus","year":"2020","journal-title":"npj Digit. 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