Argument-based human-AI collaboration for supporting behavior change to improve health

Front Artif Intell. 2023 Feb 16:6:1069455. doi: 10.3389/frai.2023.1069455. eCollection 2023.

Abstract

This article presents an empirical requirement elicitation study for an argumentation-based digital companion for supporting behavior change, whose ultimate goal is the promotion and facilitation of healthy behavior. The study was conducted with non-expert users as well as with health experts and was in part supported by the development of prototypes. It focuses on human-centric aspects, in particular user motivations, as well as on expectations and perceptions regarding the role and interaction behavior of a digital companion. Based on the results of the study, a framework for person tailoring the agent's roles and behaviors, and argumentation schemes are proposed. The results indicate that the extent to which a digital companion argumentatively challenges or supports a user's attitudes and chosen behavior and how assertive and provocative the companion is may have a substantial and individualized effect on user acceptance, as well as on the effects of interacting with the digital companion. More broadly, the results shed some initial light on the perception of users and domain experts of "soft," meta-level aspects of argumentative dialogue, indicating potential for future research.

Keywords: Human-Centered Artificial Intelligence; argumentation schemes; behavior change; digital companion; formal argumentation dialogues; health promotion; user-modeling; value-based argumentation.

Grants and funding

Research was partially funded by the Marianne and Marcus Wallenberg Foundation (Dnr MMW 2019.0220), and Wallenberg AI, Autonomous Systems and Software Program–Humanity and Society (WASP-HS). Further, the research programme grant from Forte, the Swedish Research Council for Health, Working Life and Welfare, supports STAR-C during 2019-2024 (Dnr. 2018-01461). This work was also partially funded by The Humane-AI-Net excellence network funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 952026.