Clinician Warns of Potential AI “Collusion” With Unreliable Human Input in Mental Health
Source: Freepik. Copyright: Freepik License: CCBY A new viewpoint article published in JMIR Mental Health argues that AI systems should evaluate the reliability of human training and feedback data to improve safety in mental health applications. (Toronto, May 27, 2026) A new viewpoint article published in JMIR MentalHealth warns that artificial intelligence (AI) systems used in mental health settings may inherit and reinforce unreliable human input unless new safeguards are adopted. The paper, titled “When AI Colludes: ClinicalReliability of Training and Preference Data as a Trustworthy-AI Criterion,” calls for the “clinical reliability” of training data to become a core standard for trustworthy AI. The articleRead More →

