Citation

Starke G, D'Imperio A, Ienca M. Front. Psychiatry 2023; 14: e1209862.

Copyright

(Copyright © 2023, Frontiers Media)

DOI

10.3389/fpsyt.2023.1209862

PMID

37692304

PMCID

PMC10483237

Abstract

Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions about future violent behaviour and criminal recidivism pose ethical challenges that require careful deliberation due to their social and legal significance. In this paper, we shed light on these challenges by considering externalist accounts of psychiatric disorders which stress that the presentation and development of psychiatric disorders is intricately entangled with their outward environment and social circumstances. We argue that any use of predictive AI in forensic psychiatry should not be limited to neurobiology alone but must also consider social and environmental factors. This thesis has practical implications for the design of predictive AI systems, especially regarding the collection and processing of training data, the selection of ML methods, and the determination of their explainability requirements.

Language: en

Keywords

ethics; machine learning; artificial intelligence; social determinants of health; forensic psychiatry