Introducing ELLIPS: An Ethics-Centered Approach to Research on LLM-Based Inference of Psychiatric Conditions

TL;DR

This paper introduces ELLIPS, an ethical toolkit designed to guide the development of language models for inferring psychiatric conditions, ensuring ethical considerations are integrated into research for real-world clinical application.

Contribution

The paper presents a novel ethical framework and practical toolkit, ELLIPS, to address ethical challenges in research on LLM-based inference of psychiatric conditions.

Findings

Identified seven core ethical principles for this research domain.

Developed ELLIPS, a practical tool translating principles into guiding questions.

Demonstrated the toolkit's application through a case study.

Abstract

As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models to infer neuropsychiatric conditions or psychopathological traits from language production. Yet, so far, this research has only delivered solutions with limited clinical applicability, due to insufficient consideration of ethical questions crucial to ensuring the synergy between possible applications and model design. To accelerate progress towards clinically applicable models, our paper charts the ethical landscape of research on language-based inference of psychopathology and provides a practical tool for researchers to navigate it. We identify seven core ethical principles that should guide model development and deployment in this domain, translate them into ELLIPS, an ethical toolkit operationalizing these principles into questions that can guide researchers' choices with respect to data selection, architectures, evaluation, and model deployment, and provide a case study exemplifying its use. With this, we aim to facilitate the emergence of model technology with concrete potential for real-world applicability.