A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains
This addresses the need for behavioral guarantees in foundation models for socially sensitive applications, though it appears incremental by adapting robotic techniques to a new context.
The paper tackles the problem of ensuring foundation models behave appropriately in sensitive domains like healthcare and finance by proposing a grounded observer framework that dynamically adjusts model actions based on real-time behavioral assessments, demonstrated through a system enabling casual conversations with a robot.
As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete state and action spaces, cannot be directly applied. Drawing inspiration from robotic action selection techniques, we propose the grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability. This method leverages real-time assessment of low-level behavioral characteristics to dynamically adjust model actions and provide contextual feedback. To demonstrate this, we develop a system capable of sustaining contextually appropriate, casual conversations ("small talk"), which we then apply to a robot for novel, unscripted interactions with humans. Finally, we discuss potential applications of the framework for other social contexts and areas for further research.