2025· International Scientific Conference ERAZ - Knowledge Based Sustainable Development· 0 citations· 19 references
TL;DR
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, and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.
Abstract
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.
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.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations
The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.
Emre İmamoğlu· Journal of Perspectives in M...· 0 citations
The integration of Artificial Intelligence (AI) and digital platforms into public administration and private sectors is fundamentally reshaping governance structures worldwide. While these technologies offer substantial opportunities for efficiency and innovation, they simultaneously pose significant challenges to legal accountability, institutional legitimacy, social equity, and ecological sustainability. Despite a proliferation of policy initiatives, comprehensive evidence is lacking on how AI-driven governance concretely influences policy decision-making across diverse strategic sectors. This study employed a systematic literature review (SLR) methodology, following a structured screening and selection process. From an initial corpus, 45 peer-reviewed empirical and policy-oriented articles published between 2021 and 2026 were selected for in-depth analysis. A thematic synthesis approach was applied, categorizing the literature into four interconnected analytical pillars: (i) institutional transformation and regulatory governance, (ii) access, participation, and digital justice, (iii) innovation, competitiveness, and sustainable development, and (iv) ethics, risk mitigation, and policy legitimacy. The findings reveal that digital governance operates through four primary mechanisms: restructuring institutional procedures and oversight frameworks (law and public administration); enhancing access to resources, services, and citizen participation (agrarian and socio-cultural sectors); driving economic innovation and environmental sustainability (economic and resource management); and navigating ethical risks to maintain policy legitimacy (cross-cutting). A persistent gap was identified between technological adoption and the adaptive capacity of existing legal and regulatory institutions, particularly in addressing data governance, accountability, and inclusivity challenges. The SLR demonstrates that effective AI and digital platform governance demands a holistic, context-sensitive approach that actively balances efficiency with justice, innovation with accountability, and risk with public value. The study offers an integrative framework for policymakers and practitioners to navigate digital transformation complexities and establishes a robust foundation for future empirical research on policy decision-making in the digital era.
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.
As artificial intelligence (AI) adoption accelerates, organizations must communicate its role, value, and governance to diverse stakeholders. A common response is to “get principled” and create AI principles that respond to stakeholder demands for transparent, fair, ethical, explainable, and safe AI use, while fostering employees’ AI adoption. Based on 99 publicly available AI principles from FT Global 500 companies, this study examines how large corporations use these texts as institutional messages that declare organizational beliefs and rules for AI use. We examine how they legitimize and position organizations as AI enablers. The analysis shows that AI principles function as strategic business communication that blends ethical commitments, socio-technical safeguards, and stakeholder value propositions. The study contributes to discussions on institutional theory, organizational communication, and AI governance by explaining how governance-related corporate texts construct legitimacy and organizational identity amid technological change in contemporary global corporate communication.
M. Huhta, Anu E. Sivunen, Ward van Zoonen· International Journal of Bus...· 0 citations
It is argued that AI adoption should be understood not merely as automation but as a technology-transfer problem, and the AITTF is introduced, a seven-stage governance model designed to help organisations transfer workflows, expertise and decision-making into AI-enabled systems while maintaining accountability, operational integrity and organisational memory.
Musarat Kabir-Chisty· AI and Ethics· 0 citations
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