What Makes AI Human-Centered? Identifying and Prioritizing the Attributes of Human-Centeredness: An Exploratory Study with Asia-Pacific Stakeholders

Human-Centered AI (HCAI) has emerged as a guiding paradigm for designing AI systems that align with human values, needs, and well-being, yet the field lacks consensus on what constitutes human-centeredness. This study addresses that gap through a four-phase sequential mixed-methods design: (1) thematic analysis of 81 HCAI definitions from academic, institutional, and industry sources, yielding 78 keywords; (2) frequency-based statistical categorization; (3) expert evaluation producing a final inventory of 26 attributes; and (4) a cross-sectional survey (N = 145), predominantly drawn from the Asia-Pacific region (77.2%, with Myanmar, Singapore, and Thailand most represented), in which practitioners, academics, and students rated each attribute on a 7-point Likert scale, complemented by a reflexive thematic analysis of open-ended responses. The 26-item scale demonstrated excellent internal consistency. Trust, values, benefits, needs, and usability were rated most highly, while affective and cognitive attributes—emotions, behaviours, and empathy—were consistently rated lower, a pattern the qualitative data suggest reflects perceived intractability rather than indifference. Inter-attribute correlations revealed interpretable substructures, including an experience/usability cluster, an emotion/empathy cluster, and a participatory engagement cluster, while human control operated as a conceptually independent dimension. Five qualitative themes provided interpretive context: user needs and augmentation as design drivers, ethical foundations and value alignment, trust as a relational outcome contingent on transparency, the complexity of human experience as a design challenge, and structural barriers including corporate incentives, regulatory gaps, and resource constraints. In this predominantly Southeast Asian sample, all three stakeholder groups showed substantial agreement on which attributes matter most and least. The primary divergence ran between academics and students: academics assigned higher importance to participatory and process-oriented attributes, while students emphasized tangible outcomes. Practitioners occupied an intermediate position, with a distinctive emphasis on ethical values. These findings offer an empirically grounded vocabulary for human-centeredness, positioned as an exploratory foundation for future psychometric refinement, with implications for HCAI design practice, education, and cross-stakeholder dialogue.