Beyond transparency: governance tensions and a typology of accountability gaps in artificial intelligence for high-stakes service sectors
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2026
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Virtus Interpress
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Transparency has become the shared reference point for governing artificial intelligence (AI) across regulation, corporate social responsibility (CSR) and explainability in high-stakes services, yet disclosure alone does not produce accountability (Alsulami, 2025; Al Sharif, 2026). This conceptual paper asks where, at which governance level and under which conditions, transparency fails to become accountability, formalising a typology of accountability gaps. The study is a problem-driven synthesis of a bounded corpus of 64 regulatory, organisational and technical sources (2018–2026), coded along two dimensions: governance level (regulatory, organisational, technical or system-level) and accountability condition (formal, distributed or contestable). It identifies four distinct tensions: regulation strengthens formal accountability but underspecifies responsibility and contestation; CSR and ethics frameworks reach distributed accountability when translated into roles and review; Explainable AI (XAI) affords contestability through intelligibility but not recourse; and integrating these levels—where an AI output becomes a reason for action—is the central unresolved gap. Mechanisms at one level cannot substitute for those missing at another: transparency remains necessary but becomes accountability only when connected to responsibility, evidence and contestation. These tensions are amplified in high-stakes service contexts, and the typology offers policymakers, organisations and researchers a diagnostic framework for accountable AI governance.
Palabras clave
Accountability
AI Governance
Corporate Social Responsibility
European Union Regulation
Explainability
High-Stakes Services
AI Governance
Corporate Social Responsibility
European Union Regulation
Explainability
High-Stakes Services
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Urrutia-Onate, D., Bollain, J., Onieva, E., & Perallos, A. (2026). Beyond transparency: governance tensions and a typology of accountability gaps in artificial intelligence for high-stakes service sectors. Journal of Governance and Regulation, 15(3), 75-90. https://doi.org/10.22495/JGRV15I3ART7