"Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations
Rueda, Jon (University of Granada. FiloLab Scientific Unit of Excellence)
Delgado, Janet (University of Granada)
Parra Jounou, Iris, 1989- (Universitat Autònoma de Barcelona)
Hortal-Carmona, Joaquín (University of Granada. Andalusian Health System)
Ausín, Txetxu (Spanish National Research Council. Institute of Philosophy)
Rodríguez-Arias, David (University of Granada. FiloLab Scientific Unit of Excellence)

Data:	2022
Resum:	The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients' benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.
Ajuts:	"la Caixa" Foundation LCF/BQ/ DR20/11790005
Nota:	Altres ajuts: Universidad de Granada/CBUA. This research is funded by the project "Detección y eliminación de sesgos en algoritmos de triaje y localización para la COVID-19" of the call Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID-19, en el área de Humanidades. DR-A thanks the funding of the Spanish Research Agency (codes FFI2017-88913-P and PID2020-118729RB-I00). IPJ also thanks the funding of the Spanish Research Agency (code PID2019-105422GB-I00).
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Artificial intelligence ; Distributive justice ; Explainability ; Medical AI ; Procedural fairness
Publicat a:	AI & society, december 2022, p. 1-12, ISSN 1435-5655

DOI: 10.1007/s00146-022-01614-9
PMID: 36573157

12 p, 806.6 KB

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