<![CDATA[This study develops a governance model for Explainable Artificial Intelligence (XAI) in a stunting early warning system by integrating a socio-technical approach and the Technology Acceptance Model (TAM). The main issues examined are the low transparency of AI systems, the lack of structure in health AI governance, and the need to build user trust before predictive systems are used in public health services. The research method employed a mixed-methods approach, consisting of a quantitative survey of 100 respondents and semi-structured interviews with 10 informants from the Health Department, Community Health Centers (Puskesmas), the Communication and Information Department (Diskominfo), midwives, and Posyandu cadres. Quantitative data were analyzed using multiple linear regression, while qualitative data were used to strengthen the socio-technical interpretation. The results indicate that XAI and governance have a positive influence on trust. Furthermore, trust and perceived usefulness have a positive influence on behavioral intention. The resulting model identifies transparency, accountability, security, compliance, periodic validation, audits, and feedback as governance mechanisms that link the technical quality of AI with user acceptance. The contribution of this research is a conceptual model of XAI governance that can serve as the basis for developing a transparent, accountable, and user-accepted early warning system for stunting. Keywords – Behavioral Intention; Explainable AI; Socio-Technical; Stunting; Governance. ]]>
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