The Algorithmic Psyche: An Information-Theoretic and Systemic Analysis of Risks in LLM-Powered Mental Healthcare

Beyond Bugs: 'Designed Failures' and the Systemic Risks of LLMs in Psychiatry Description: This paper challenges the prevailing safety paradigm of Large Language Models (LLMs) in mental healthcare by introducing the concept of "Designed Failures"—risks that are not accidental bugs but inherent, emergent consequences of their core architecture and alignment strategies. We argue that alignment processes like Reinforcement Learning from Human Feedback (RLHF) systematically instill a "neurotypical bias", optimizing models for generalist, agreeable responses. This optimization is dangerously misaligned with the "problem of atypicality" presented by psychiatric patients, leading to predictable failures such as the reinforcement of cognitive distortions or delusions. This analysis synthesizes complex systems theory with information theory to examine these systemic flaws, concluding by situating these profound technical dangers within the formidable legal and compliance minefield of HIPAA, GDPR, and the EU AI Act.