PERSPECTIVE article
Front. Psychiatry
Sec. Psychopathology
Scaling Injustice: Epistemic Harm in DID and What Clinical Records Will Teach AI
Provisionally accepted
- 1University of Virginia, Charlottesville, United States
- 2McLean Hospital, Belmont, United States
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Dissociative Identity Disorder (DID) remains one of psychiatry's most doubted diagnoses, where patients' accounts are dismissed and their experiences forced into ill-fitting diagnostic categories. This article examines how testimonial and hermeneutical injustices manifest in clinical practice, from skepticism about the disorder's validity to documentation that renders patients' trauma histories incoherent. These failures delay accurate diagnosis, erode therapeutic alliances, and create clinical records that now train artificial intelligence systems. As AI tools increasingly shape psychiatric decision-making, we face an urgent reality: if clinicians cannot recognize or document complex trauma accurately, automated systems will scale these failures exponentially. Drawing on DID research and epistemic justice frameworks, I argue for immediate reforms in clinical documentation, psychiatric training, and data governance to prevent algorithmic amplification of longstanding harms.
Keywords: Artificial Intelligence in Psychiatry, Clinical documentation, Diagnostic bias, Dissociative Identity Disorder, Electronic Health Records, Epistemic injustice and psychiatry, Trauma-informed care
Received: 29 Jan 2026; Accepted: 16 Feb 2026.
Copyright: © 2026 Akinlade. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Oluwafunmilayo Akinlade
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