Trust-centred ethical data governance in artificial intelligence ecosystems

Purpose The rapid adoption of Artificial Intelligence (AI) into digital business platforms has led to growing concerns surrounding data privacy, algorithmic transparency and user trust. This study aims to explore how responsible data practices can be implemented within AI-powered innovation systems, where regulatory enforcement and jurisdictional frameworks remain inconsistent. Design/methodology/approach The study provides an overview based on a review of 79 journal articles published during 2011–2024. It examines how privacy, innovation and trust have been addressed in organisational contexts, and how these issues have been framed across geographic regions and thematic clusters. Privacy laws such as general data protection regulation (GDPR) and central consumer protection authority (CCPA) are considered in relation to how they influence organisational responses to data governance challenges. The review also identifies persistent gaps between formal compliance and meaningful consumer empowerment, and uses these patterns to inform framework development. Findings The review concludes that while compliance-based approaches are necessary, they are insufficient to sustain trustworthiness in data-driven and AI-mediated settings characterised by information asymmetries, trans-border data flows and shifting notions of accountability. Privacy-by-Design (PbD), explainable AI (XAI) and privacy-enhancing technologies (PETs) are highlighted as governance enablers that support more dynamic and trust-oriented approaches. Drawing on Regulatory Governance, Trust Theory and AI Ethics, the study develops an integrated framework that reinterprets ethical data governance as a trust-centred governance capability rather than a narrow compliance exercise. In doing so, it elevates trust as a central object of governance and reframes privacy as an organisational capability that helps firms navigate fragmented regulatory environments while sustaining legitimacy and innovation. Originality/value This research contributes by synthesising fragmented debates on governance, trust, privacy and AI ethics, and by reframing ethical data governance as a trust-centred governance capability. Its originality is therefore integrative and practical rather than based on wholly new theoretical mechanisms. The paper explains how regulatory diversity, organisational accountability, PbD and technical safeguards interact under conditions of cross-border data flows and technological complexity. In doing so, it provides a framework that can guide enterprises, regulators and public institutions in managing AI ethically across jurisdictions while strengthening trust as a core governance objective.