Accomodating User Expressivity while Maintaining Safety for a Virtual Alcohol Misuse Counselor

Client-centered counseling, in which individuals are prompted to talk about their behavior, is the standard treatment for Alcohol misuse. However, open-ended conversations with virtual agent counselor raise potential safety concerns if the agent misunderstands and provides erroneous advice. Thus, while generative machine learning models have been successful for language understanding and generation tasks, these approaches may not be effective or safe for counseling. We present a hybrid dialog system that uses a machine-learning model to generate responses to individual client speech combined with a rule-based approach to transition through structured counseling sessions. The dialog system is used to drive a virtual agent alcohol misuse counselor. We evaluated this hybrid system by comparing it to a functionally equivalent system in which the dialog is driven by fully-constraining user utterances via multiple-choice menus among individuals with problematic drinking. Participants who interacted with the agent using the hybrid dialog system reported a higher degree of readiness to change their drinking habits compared to the system using constrained input. Additionally, the outputs of the models used by the system were judged to be safe and appropriate for the task by expert counselors.