Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

Abstract

The increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the field, to identify existing problems, and to address them with fine-grained ethical reflection. We conducted semi-structured qualitative interviews with 15 experts from Germany and Switzerland with training in medicine and neuroscience on the assistive use of machine learning in psychiatry. We used reflexive thematic analysis to identify key ethical expectations and attitudes towards machine learning systems. Experts’ ethical expectations towards machine learning in psychiatry partially challenge orthodoxies from the field. We relate these challenges to three themes, namely (1) ethical challenges of machine learning research, (2) the role of explainability in research and clinical application, and (3) the relation of patients, physicians, and machine learning system. Participants were divided regarding the value of explainability, as promoted by recent guidelines for ethical artificial intelligence, and highlighted that explainability may be used as an ethical fig leaf to cover shortfalls in data acquisition. Experts recommended increased attention to machine learning methodology, and the education of physicians as first steps towards a potential use of machine learning systems in psychiatry. Our findings stress the need for domain-specific ethical research, scrutinizing the use of machine learning in different medical specialties. Critical ethical research should further examine the value of explainability for an ethical development of machine learning systems and strive towards an appropriate framework to communicate ML-based medical predictions.