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Towards a pragmatist dealing with algorithmic bias in medical machine learning
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 4618283
Author(s) Starke, Georg; De Clercq, Eva; Elger, Bernice S.
Author(s) at UniBasel Starke, Georg
De Clercq, Eva
Elger, Bernice Simone
Year 2021
Title Towards a pragmatist dealing with algorithmic bias in medical machine learning
Journal Medicine, Health Care and Philosophy
Volume 24
Number 3
Pages / Article-Number 341-349
Keywords Algorithmic bias; Artificial intelligence; Fairness; Machine learning; Philosophy of Science; Pragmatism
Abstract Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James's pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine.
Publisher Kluwer Academic
ISSN/ISBN 1386-7423 ; 1572-8633
Full Text on edoc No
Digital Object Identifier DOI 10.1007/s11019-021-10008-5
PubMed ID
ISI-Number WOS:000628504200001
Document type (ISI) Journal Article

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