Framing Responsible Design of AI Mental Well-Being Support: AI as Primary Care, Nutritional Supplement, or Yoga Instructor?

Abstract: Millions of people now use non-clinical LLM tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool's "active ingredients" for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool's pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies - LLM tools as supplements, drugs, yoga instructors, and primary care providers - can scaffold further conversations about their responsible design.

• The paper finds that clear specification of target users, benefits, and risk management is essential for responsible design.
• The methodology combines expert interviews, policy analysis, and iterative synthesis to develop four regulatory analogies.
• The research underscores balancing risks with claimed benefits while addressing societal impacts such as health inequities.

Responsible Design in AI Mental Well-Being Support: Conceptualizing LLM Tools as Primary Care, Nutritional Supplements, or Yoga Instructors

Introduction

The paper "Framing Responsible Design of AI Mental Well-Being Support: AI as Primary Care, Nutritional Supplement, or Yoga Instructor?" (2602.02740) articulates a nuanced framework for the responsible design and evaluation of non-clinical LLM-based mental well-being tools. Drawing upon expert interviews and extensive policy analysis, the paper characterizes how regulatory analogies (e.g., over-the-counter medications, nutritional supplements, yoga instruction, and primary care) can scaffold actionable standards for responsibility in the emerging context of AI-mediated mental self-care. The research critically examines current industry practice, empirical and theoretical risks, and the limitations of existing accountability mechanisms, providing a practical and theoretically grounded synthesis for HCI and AI researchers.

Background and Motivation

Non-clinical LLM tools such as ChatGPT and Replika are increasingly utilized for mental well-being and self-care, targeting users without diagnosed mental illness or acute suicidal ideation. While these tools can expand access, reduce stigma, and partially address provider shortages, significant risks—ranging from rare but catastrophic suicidality events to displacement of necessary clinical intervention to the exacerbation of health inequities—are present. The case for regulatory and design responsibility is strengthened by growing empirical evidence showing that high LLM engagement correlates with increased loneliness, decreased socialization, and potential miscalibration of mental health beliefs (2602.02740).

Standard evaluation methods (clinical trials, diagnostic metrics, perceived usefulness) are not directly translatable to the diverse and rapidly evolving landscape of non-clinical LLM interventions. Prior attempts to impose FDA-level clinical standards have proven economically and logistically infeasible, with several major digital therapeutics companies failing during the regulatory process. Conversely, approaches relying solely on user warnings and disclaimers have been largely ineffective. This context motivates a structured reframing of responsible design away from binary clinical/consumer dichotomies.

Methodological Overview

The study proceeds via three methodological axes:

1. Expert Interviews: Twenty-four domain experts in responsible AI, policy, medical ethics, and digital therapeutics were interviewed to capture multi-disciplinary understandings of responsible design and actionable evaluation.
2. Policy Analysis: Over one hundred policy documents were analyzed to map the regulatory paradigms for nutritional supplements, pharmaceuticals, and non-clinical services, including primary care and yoga instruction.
3. Deliberative Synthesis: Iterative interviews and validation with experts subjected the emergent themes and analogies to further critique and consensus-building, particularly emphasizing disciplinary divergences in risk/benefit assessment and standards justification.

Key Analytic Framework: The Four Analogies

Four analogies were derived as scaffolds for responsibility, orienting both expected benefits and associated risks:

• Nutritional Supplement: Minimal guaranteed benefits, minimal risks, no claims of disease mitigation or cure. Responsibility focusses on honest representation and avoidance of misuse as therapy substitute.
• Over-the-Counter Drug: Tools claiming symptom relief or specific functional improvement are accountable for clinical-grade safety and efficacy, as well as equitable access. Evaluation is aligned with established medical risk/benefit trade-offs.
• Yoga Instructor: Service-like offerings promote practices (mindfulness, self-reflection) with evidence-based but non-guaranteed outcomes, and instructor liability is limited. These bear similarities to journaling or mindfulness LLM apps without explicit clinical targets.
• Primary Care Provider: Services making strong guarantees of benefit and engaging in triage or crisis detection assume high liability and must ensure effective delivery and escalation for high-risk scenarios, as in detection and referral for suicidality.

Main Findings

1. Responsibility Hinges on Claimed Benefit and User Target

Design responsibility is operationalized by clearly articulating what benefit is guaranteed, to which user population, and with what mechanism. Generalized claims (e.g., 'improves mental well-being') are insufficient. Tools offering more specific, interventionist utility (e.g., alleviating depressive symptoms via CBT) must meet higher standards for safety, effectiveness, and health equity, while wellness-promoting tools are primarily accountable for managing indirect risks—such as misuse, displacement of genuine self-care, and overuse.

2. Articulation and Delivery of "Active Ingredients"

A responsible LLM tool must identify its "active ingredient" (e.g., validated intervention, coping strategy) and demonstrate, or guarantee within operational bounds, the effective delivery to users. Failure to specify or document these mechanisms (i.e., “fun” without structure, as in social media) results in incalculable risk and is not considered responsible design. Tools paralleling primary care must also assure escalation for users exhibiting high-risk signals (e.g., suicidality), while supplement/yoga analogies require mechanisms to prevent inappropriate substitution for clinical care.

3. Commensurate Risks and Benefits, and Disciplinary Value Divergences

There is consensus that responsible design requires balancing risk proportional to the claimed benefit, but strong disagreement emerges regarding the population-level calculus. Medical and policy experts often justify rare severe outcomes (e.g., idiosyncratic suicide risk) if population benefit is substantial and risks are disclosed (as with breakthrough drugs). Design and ethics scholars express reservations, challenging whether purely utilitarian, aggregate-risk frameworks are appropriate for mental health AI tools. Additionally, whether supplement-like LLM tools actually constitute responsible design—given their low utility and potential for indirect harm—remains contentious.

Evaluation Criteria and Practical Recommendations

The following design and evaluation actions are posited as minimum requirements:

• Explicit Targeting and Benefit Specification: Define both target users and specific, measurable, reliable outcomes.
• Transparent Active Ingredient Identification: Clearly document the mechanism of action and support with available evidence.
• Risk Management Structure: For supplement/yoga analogies, implement robust preventative features to avoid misuse and displacement; for over-the-counter/primary care analogies, ensure clinical rigor, health equity, and reliable escalation protocols for high-risk users.
• Communication and Labelling: Accurate expectation management via interface messaging and user education, informed by analogs such as FDA disclosure and nutritional labels.
• Evaluation Beyond Diagnostic Efficacy: Societal harms (e.g., loneliness, self-care displacement, inequity exacerbation) must be primary endpoints in addition to traditional clinical or user satisfaction measures.

Implications and Open Problems

Theoretical Implications

The research bridges human-computer interaction with regulatory theory, operationalizing nuanced accountability in contexts where traditional device/therapeutic models break down. The analogy-driven framework provides a practical taxonomy, aligning responsibility, evaluation metric selection, and actionable design principles in a way that is directly implementable yet remains sensitive to societal risks identified through expert critique.

Practical Implications

Immediate design adoption is advised for:

• Reinstating foundational HCI practice (clear target/user/benefit specification) in LLM tool development.
• Building interactive features to prevent inappropriate use as therapy substitutes.
• Constructing evaluation scaffolds that prioritize both direct outcomes and indirect societal impacts.

Future Research Directions

• Dynamic Adaptation: Developing systems capable of fluidly adapting benefit promises and responsibility profiles as user states change.
• Health Equity Analytic Tooling: Automated mechanisms for stratifying, monitoring, and guaranteeing equitable benefit distribution.
• Higher-Order Accountability: Investigating whether rights-based or value-sensitive approaches can supersede commensurate risk/benefit models.
• Active Ingredient Databases: Standardizing and publicizing evidence for AI-deliverable mechanisms for mental well-being, akin to clinical intervention repositories.
• Population vs. Individual Risk: Deliberating, both epistemically and ethically, the appropriateness of population-level risk frameworks for mental health AI.

Conclusion

This paper provides a theoretically robust and practically actionable model for responsible LLM tool design in mental well-being, emphasizing the necessity of analogical reasoning in operationalizing accountability where extant regulatory paradigms are either infeasible or insufficient. By mapping domain-specific risk/benefit matrices and advancing clear design and evaluation standards, the framework empowers HCI and AI researchers, tool designers, and regulators to deliberate more precisely on the locus and structure of responsibility, with tangible directions for advancing both safety and efficacy in AI-driven self-care ecosystems (2602.02740).