What is critical for human-centered AI at work? - Toward an interdisciplinary theory

Area of Science:

• Artificial Intelligence
• Human Factors
• Cognitive Science

Background:

• Human-centered artificial intelligence (HCAI) lacks clear definition, with disciplinary variations hindering its application.
• Disciplinary differences in HCAI scope necessitate a systematic mapping, particularly within the work context.

Purpose of the Study:

• To compare HCAI conceptualizations across Human Factors and Ergonomics (HFE), psychology, Human-Computer Interaction (HCI), information science, and adult education.
• To discuss normative, theoretical, and methodological approaches to HCAI and their implications for research and practice.
• To establish the foundation for a theory of human-centered interdisciplinary AI.

Main Methods:

• Comparative analysis of HCAI perspectives from diverse academic disciplines.
• Identification of commonalities and differences in theoretical and methodological approaches.
• Synthesis of key aspects for successful HCAI implementation in the workplace.

Main Results:

• Significant disciplinary differences exist in HCAI scope, particularly concerning the work context.
• An interdisciplinary approach is crucial for developing, transferring, and implementing HCAI effectively.
• Key HCAI aspects include human capability, controllability, autonomy, trust, learning designs, and information literacy.

Conclusions:

• An interdisciplinary approach is critical for advancing HCAI in the workplace.
• Disciplines like HFE, psychology, HCI, information science, and adult education offer essential perspectives for HCAI.
• The study introduces the Synergistic Human-AI Symbiosis Theory (SHAST) framework for human-centered interdisciplinary AI.