Towards Dialogue Systems with Agency in Human-AI Collaboration Tasks

Agency, the capacity to proactively shape events, is crucial to how humans interact and collaborate with other humans. In this paper, we investigate Agency as a potentially desirable function of dialogue agents, and how it can be measured and controlled. We build upon the social-cognitive theory of Bandura (2001) to develop a framework of features through which Agency is expressed in dialogue -- indicating what you intend to do (Intentionality), motivating your intentions (Motivation), having self-belief in intentions (Self-Efficacy), and being able to self-adjust (Self-Regulation). We collect and release a new dataset of 83 human-human collaborative interior design conversations containing 908 conversational snippets annotated for Agency features. Using this dataset, we explore methods for measuring and controlling Agency in dialogue systems. Automatic and human evaluation show that although a baseline GPT-3 model can express Intentionality, models that explicitly manifest features associated with high Motivation, Self-Efficacy, and Self-Regulation are better perceived as being highly agentive. This work has implications for the development of dialogue systems with varying degrees of Agency in collaborative tasks.