Human-Centered AI at Work: Common Ground in Theories and Methods

Human-centered artificial intelligence (HCAI) has gained momentum in the scientific discourse but still lacks clarity. In particular, disciplinary differences regarding the scope of HCAI have become apparent and were criticized, calling for a systematic mapping of conceptualizations—especially with regard to the work context. This article compares how human factors and ergonomics (HFE), psychology, human-computer interaction (HCI), information science, and adult education view HCAI and discusses their normative, theoretical, and methodological approaches toward HCAI, as well as the implications for research and practice. It will be argued that an interdisciplinary approach is critical for developing, transferring, and implementing HCAI at work. Additionally, it will be shown that the presented disciplines are well-suited for conceptualizing HCAI and bringing it into practice since they are united in one aspect: they all place the human being in the center of their theory and research. Many critical aspects for successful HCAI, as well as minimum fields of action, were further identified, such as human capability and controllability (HFE perspective), autonomy and trust (psychology and HCI perspective), learning and teaching designs across target groups (adult education perspective), as much as information behavior and information literacy (information science perspective). As such, the article lays the ground for a theory of human-centered interdisciplinary AI, i.e., the Synergistic Human-AI Symbiosis Theory (SHAST), whose conceptual framework and founding pillars will be introduced.

Introduction: With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.

Methods: The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.

Results: Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.

Discussion: We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.