The Agony of Opacity: Foundations for Reflective Interpretability in AI-Mediated Mental Health Support

TL;DR

This paper advocates for a reflective interpretability framework in AI mental health chatbots, emphasizing user agency and interpretative engagement to address the unique psychological needs of severely distressed individuals.

Contribution

It introduces a novel reflective interpretability approach tailored for AI mental health support, integrating practices from mental health fields to enhance user understanding and agency.

Findings

Proposes design strategies like role induction and advance directives.

Highlights importance of interpretability for severely distressed users.

Discusses risks and mitigation in AI mental health support.

Abstract

Throughout history, a prevailing paradigm in mental healthcare has been one in which distressed people may receive treatment with little understanding around how their experience is perceived by their care provider, and in turn, the decisions made by their provider around how treatment will progress. Paralleling this offline model of care, people who seek mental health support from artificial intelligence (AI)-based chatbots are similarly provided little context for how their expressions of distress are processed by the model, and subsequently, any reasoning or theoretical grounding that may underlie model responses. People in severe distress who turn to AI chatbots for support thus find themselves caught between black boxes, contending with unique forms of agony that arise from these intersecting opacities. In this paper, we argue that the distinct psychological state of individuals experiencing severe mental distress uniquely necessitates a higher standard of end-user interpretability in comparison to general AI chatbot use. We propose a reflective interpretability approach to AI-mediated mental health support, which nudges users to engage in an agency-preserving and iterative process of reflection and interpretation of model outputs, towards creating meaning from interactions (rather than accepting outputs as directive instructions). Drawing on interpretability practices from four mental health fields (psychotherapy, crisis intervention, psychiatry, and care authorization), we describe concrete design approaches for reflective interpretability in AI-mediated mental health support, including role induction, prosocial advance directives, intervention titration, and well-defined mechanisms for recourse, alongside a discussion of potential risks and mitigation measures.