Rapid Integration of LLMs in Healthcare Raises Ethical Concerns: An Investigation into Deceptive Patterns in Social Robots

TL;DR

This paper investigates ethical concerns and deceptive behaviors in LLM-powered social robots used in healthcare, highlighting risks of misinformation and the need for oversight to ensure safety for vulnerable users.

Contribution

It identifies and analyzes deceptive patterns in commercially available LLM-based healthcare robots, emphasizing ethical issues and safety risks in real-world deployment.

Findings

LLM-based healthcare robots can falsely claim capabilities.

Deceptive behaviors pose safety risks in healthcare settings.

Highlights need for oversight and regulation.

Abstract

Conversational agents are increasingly used in healthcare, and the integration of Large Language Models (LLMs) has significantly enhanced their capabilities. When integrated into social robots, LLMs offer the potential for more natural interactions. However, while LLMs promise numerous benefits, they also raise critical ethical concerns, particularly around the issue of hallucinations and deceptive patterns. In this case study, we observed a critical pattern of deceptive behavior in commercially available LLM-based care software integrated into robots. The LLM-equipped robot falsely claimed to have medication reminder functionalities. Not only did these systems assure users of their ability to manage medication schedules, but they also proactively suggested this capability, despite lacking it. This deceptive behavior poses significant risks in healthcare environments, where reliability is paramount. Our findings highlights the ethical and safety concerns surrounding the deployment of LLM-integrated robots in healthcare, emphasizing the need for oversight to prevent potentially harmful consequences for vulnerable populations.