Toward LLM-Powered Social Robots for Supporting Sensitive Disclosures of Stigmatized Health Conditions

TL;DR

This paper discusses the potential and challenges of using large language model-powered social robots to support individuals in disclosing stigmatized health conditions like HIV, highlighting opportunities, risks, and ethical considerations.

Contribution

It provides a comprehensive analysis of the technical, ethical, and safety considerations for deploying LLM-powered social robots in sensitive health disclosures, focusing on HIV status.

Findings

Identifies key opportunities for LLM social robots in health disclosures

Highlights ethical and safety concerns in deploying such robots

Examines technical challenges and risks involved

Abstract

Disclosing sensitive health conditions offers significant benefits at both individual and societal levels. However, patients often face challenges due to concerns about stigma. The use of social robots and chatbots to support sensitive disclosures is gaining traction, especially with the emergence of LLM models. Yet, numerous technical, ethical, privacy, safety, efficacy, and reporting concerns must be carefully addressed in this context. In this position paper, we focus on the example of HIV status disclosure, examining key opportunities, technical considerations, and risks associated with LLM-backed social robotics.