arXiv:2607.07766v1 Announce Type: new Abstract: Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychological support often requires. Developers' safety responses have been largely reactive, addressing the most visible and acute harms while subtler, longer-term patterns of risk (e.g., dependency, boundary erosion, the amplification of distorted beliefs) receive less attention. We contend that making LLMs
The proliferation of LLMs in sensitive applications like mental health support necessitates a move beyond reactive safety responses towards proactive, ethical frameworks given their increasing societal integration.
A strategic reader should care because this proposes a new standard for AI assurance, which will profoundly impact regulation, development, and public trust in AI, particularly in critical sectors like healthcare.
The focus for AI safety shifts from merely addressing acute harms to proactively preventing subtle, long-term risks, potentially leading to more robust ethical guidelines and development practices.
- · AI ethicists and safety researchers
- · Healthcare providers using AI for support
- · Regulatory bodies developing AI standards
- · Patients and users of AI mental health support
- · AI developers prioritizing engagement over safety
- · Companies with reactive-only safety protocols
- · Unregulated AI mental health platforms
- · Startups unable to meet new alignment standards
Increased scrutiny and demand for 'aligned' AI models in all sensitive applications.
Development of specialized AI auditing firms and certification processes for 'alignment plaid'.
The concept of 'alignment' expands from technical safety to encompassing broader societal and psychological well-being, driving a new wave of human-centered AI design principles.
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Read at arXiv cs.AI