Beyond transparency: governance tensions and a typology of accountability gaps in artificial intelligence for high-stakes service sectors

Vista sencilla de ítem
dc.contributor.authorUrrutia Oñate, Dorleta
dc.contributor.authorBollain Urbieta, Julen
dc.contributor.authorOnieva Caracuel, Enrique
dc.contributor.authorPerallos Ruiz, Asier
dc.date.accessioned2026-09-11T14:25:39Z
dc.date.available2026-09-11T14:25:39Z
dc.date.issued2026
dc.date.updated2026-09-11T14:25:39Z
dc.description.abstractTransparency 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.en
dc.identifier.citationUrrutia-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
dc.identifier.doi10.22495/JGRV15I3ART7
dc.identifier.eissn2306-6784
dc.identifier.issn2220-9352
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6635
dc.language.isoeng
dc.publisherVirtus Interpress
dc.rightsCopyright © 2026 The Authors
dc.subject.otherAccountability
dc.subject.otherAI Governance
dc.subject.otherCorporate Social Responsibility
dc.subject.otherEuropean Union Regulation
dc.subject.otherExplainability
dc.subject.otherHigh-Stakes Services
dc.titleBeyond transparency: governance tensions and a typology of accountability gaps in artificial intelligence for high-stakes service sectorsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage90
oaire.citation.issue3
oaire.citation.startPage75
oaire.citation.titleJournal of Governance and Regulation
oaire.citation.volume15
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
urrutia_beyond_2026.pdf
Tamaño:
480.45 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos