Jul 2026· Business Strategy and the Environment· 0 citations· 76 references
TL;DR
It is argued that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems and proposed adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories.
Abstract
Artificial intelligence (AI) has become essential to corporate decision‐making, yet current environmental, social, and governance (ESG) frameworks offer limited tools for assessing algorithmic responsibility. This paper examines whether AI's distinctive features, namely, lack of transparency, delegated agency, and ongoing adjustment, challenge the organizational reasoning of ESG frameworks. Drawing on organizational theory and science and technology studies (STS), we argue that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems. While integration succeeded for issues such as cybersecurity and climate risk, AI differs because algorithms operate as social and technical systems that distribute responsibility across human and non‐human networks. We propose adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories. It is intended as a conceptual extension of investor‐facing ESG architectures rather than a replacement of existing standards. This pillar highlights fairness, transparency, responsibility, and robustness as core dimensions of corporate responsibility. The paper contributes to organizational theory by conceptually examining conditions under which established governance architectures require structural extension and to technology governance by rethinking responsibility in mixed human‐algorithmic systems. We further discuss how algorithmic risks may vary across environmental, social, and governance domains and outline conceptual approaches for handling heterogeneity, sectoral differences, and data constraints.
The widespread application of artificial intelligence (AI) in corporate resource planning and public decision-making has provided impetus for improving management efficiency and creating social value. However, the complex structure and opacity of algorithms have led to a crisis of trust, posing challenges to traditional public management accountability mechanisms. Drawing on socio-technical systems theory, this paper provides a normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation. The findings suggest that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance. Although the EU AI Act proposes a preliminary form of collaborative governance, it still has shortcomings in terms of procedural justice and the feasibility of human oversight. The governance logic should shift from individual oversight to an organization-in-the-loop approach, to achieve sustainable and responsible AI governance through the construction of a procedural justice framework.
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
The governance of artificial intelligence (AI) in Africa faces competing pressures from demands for regulatory intervention alongside concerns about institutional capacity, innovation costs, and economic vulnerability. Debates surrounding algorithmic discrimination, biometric surveillance, and extractive data practices by global platforms have sharpened questions about the appropriate roles of states, markets, and civil society in governing AI systems. Yet existing governance scholarship tends to address these questions through either ethical principles or state-centric regulatory frameworks, leaving a significant analytical gap where law and technical design intersect.
This article introduces legal-technical governance as an analytical framework for examining Africa’s emerging AI regulatory landscape. Distinguishing governance from regulation, the article argues that AI governance in Africa is already distributed across data protection statutes, fintech guidelines, cybersecurity frameworks, and content moderation policies imposed by global platforms, making a broader, systems-level analytical tool both necessary and timely. Legal-technical governance foregrounds the co-constitutive relationship between legal norms and technical operations, including data labelling, model training, and algorithmic auditing. It accounts for the various actors shaping AI outcomes, from multinational technology firms to standards bodies and affected communities.
To examine the gaps where legal frameworks and technical systems diverge, the article draws on Dooyeweerd's modal aspects as a philosophical lens. Applied to African AI governance domains, this framework reveals how institutional fragmentation, infrastructural dependency, and global platform dominance undermine state-centred regulatory models. The article concludes by advancing legal-technical governance as a productive framework for scholarship and policymaking at the intersection of law, technology, and development in Africa.
Chijioke I. Okorie· Potchefstroom Electronic Law...· 0 citations
This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
Artificial intelligence has moved from a specialised technical concern to a central object of international economic and security policy, yet global governance arrangements remain fragmented across competing regulatory models. This article addresses how policymakers can reconcile innovation, competitiveness, human rights, security and sustainability within a coherent governance architecture for artificial intelligence operating across national, regional and multilateral levels. The goals include the review of the governance theory and current practices applicable to AI regulations, analysis of stakeholders' interests and influence, estimation of the possible economic, social, legal, technological and environmental effects of such an arrangement, as well as the design of a feasible multi-level governance system with monitoring and evaluation mechanisms. The article utilises a qualitative comparative policy analysis based on primary legal documents, which include Regulation (EU) 2024/1689, OECD Recommendation on Artificial Intelligence (as amended in 2024), UN Resolution A/RES/79/325 of 2025, and the Global Digital Compact of 2024, in addition to the academic literature on the subject from peer-reviewed sources and books. This paper proposes a framework that takes into account the multi-level and adaptive governance approaches with a particular emphasis on digital sovereignty, thereby creating a three-tier architecture that will include global normative coordination, regional and plurilateral regulatory clusters, and national or sectoral implementation, illustrated by the case study of Kenya, which has created its National Artificial Intelligence Strategy 2025-2030. The main findings in the paper suggest that regulation fragmentation among the European Union, the United States, and China is growing rather than converging, that multilateral instruments do not possess any kind of binding enforcement mechanism, and that low- and middle-income countries like Kenya experience capacity limitations even when developing a proactive national strategy. This paper argues that a layered subsidiarity approach, with specific financing for capacity-building and technical standards that work across the board, is more likely to deliver effective global AI governance than calls for a binding treaty.
Unknown authors· East African Journal of Info...· 0 citations
BackgroundThe rapid integration of artificial intelligence (AI) into labour markets, migration governance, and social protection systems is increasingly reshaping how institutional decisions are produced, delegated, and enforced. While human-centric and ethics-based AI frameworks have established important normative principles, concerns regarding inequality, opacity, and accountability in AI-mediated decision-making continue to persist across labour-related environments.ObjectiveThis article examines why ethical approaches alone are insufficient to address the structural effects of AI once algorithmic systems become embedded within institutional governance architectures operating at scale.MethodsThe article draws on institutional and policy analysis, labour and migration governance literature, and illustrative examples of algorithmic decision-making in public-sector and labour-related systems.ResultsThe analysis conceptualises AI not merely as a technical tool, but as part of the operational infrastructure through which institutional visibility, discretion, prioritisation, and authority are organised. The article demonstrates how labour markets and migration governance function as "stress-test" domains in which continuous classification, automated risk assessment, worker scoring, and fragmented data environments can amplify existing structural inequalities. It further argues that human oversight frequently becomes procedural rather than substantive once algorithmic systems operate under conditions of scale, speed, and administrative complexity.ConclusionsThe article concludes that the governance challenges associated with AI in the world of work are not primarily ethical in nature, but institutional and architectural. Advancing fairness and accountability in AI-mediated environments therefore requires institutionally grounded governance architectures, meaningful contestability mechanisms, and enforceable operational oversight beyond ethics-first compliance frameworks.
A. Sinchev, Svetlana Bekmambetova· Work· 0 citations