Jul 2026· Journal of Ethics in Entrepreneurship and Technology· 0 citations· 32 references
TL;DR
Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.
Abstract
This study aims to examine how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe, focusing on six Portuguese firms in energy, urban mobility and finance.
Adopting a sociotechnical perspective, this research uses a qualitative multiple case study design with semi-structured interviews of Chief Information Officers across diverse organizational contexts. It integrates technical and social dimensions to capture how digital infrastructures, governance practices and human factors interact in decision-making processes.
The findings reveal that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance (ESG) oriented indicators, enhancing transparency and reliability. However, maturity varies by sector, resources and technology and challenges such as data limitations, organizational resistance and regulatory uncertainty persist. Furthermore, AI governance emerges as an adaptive, iterative capability for navigating sustainability complexities.
This study provides original insights by linking AI governance to sustainable decision-making through a sociotechnical lens, an area still underexplored in empirical research. It advances theory by integrating ESG considerations into AI governance and offers practical value by identifying mechanisms that enhance transparency, accountability and sustainability outcomes.
A lifecycle-oriented socio-technical governance capacity framework through a structured synthesis of public administration, digital government, decision support systems, responsible AI, socio-technical systems, sustainability, and risk governance scholarship is developed.
This study advances a governance-centred framework for understanding how artificial intelligence contributes to urban sustainability transitions. Drawing on systematic thematic analysis of 80 primary policy documents across five global cities—Singapore, Amsterdam, Barcelona, Seoul, and Toronto—together with a broader evidence base of 170 sources combining this primary corpus with the academic and policy literature cited throughout, the study argues that AI contributes to sustainable outcomes primarily by transforming governance structures rather than through technical optimisation alone. The study identifies four institutional conditions that determine whether the sustainability dividend of AI deployment is realised: regulatory coherence, multi-stakeholder participation, transparency and auditability of algorithmic decision-making, and adaptive governance capacity. A comparative framework is developed to assess AI governance maturity across urban sustainability domains, including energy management, climate adaptation, mobility, and green public procurement. The study further examines how AI governance systems address the accountability requirements arising from algorithmic opacity, using the South Tyrol and Estonia cases to identify the conditions under which distributed ledger technologies can strengthen audit trail mechanisms in urban sustainability governance. Findings suggest that governance design—not AI capability—is the primary determinant of sustainable transformation outcomes. The study contributes to the emerging interdisciplinary literature at the intersection of AI governance, smart city development, and organisational sustainability, offering a practitioner-relevant framework for policymakers, city managers, and business leaders engaged in AI-enabled green transformation.
As artificial intelligence (AI) systems become increasingly embedded in the structures of knowledge-based organizations, the governance of AI-related risks is emerging as a critical factor for long-term systemic sustainability. This paper explores how AI risk governance can be effectively integrated into the epistemological and structural foundations of such organizations through the lens of fourth-order cybernetics. This theoretical framework emphasizes reflexivity, ethical co-construction, and multilevel feedback involving both human and technical agents. Rather than treating governance as a static set of compliance measures, the proposed model presents it as a dynamic and participatory process. Four core principles are introduced: multilevel feedback, contextual ethics, recursive governance, and the inclusion of marginalized perspectives. These principles support the embedding of AI governance into decision-making and knowledge management systems. The paper contributes to responsible innovation discourse and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.
Ludmila Jiříčková, Petr Doucek· International Scientific Con...· 0 citations
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations