Rethinking the future

Journal of Perspectives in Management

Artificial Intelligence Governance and the Future of Human Societies: Dignity, Meaning and Civilizational Responsibility

The Anthropological, Spiritual and Civilizational (ASC) Framework is proposed as a diagnostic heuristic for extending trustworthy AI toward dignity, truth, social justice and humane futures, which requires future empirical and expert validation.

Addressing the Artificial Intelligence Governance Gap in Environmental, Social, and Governance Standards

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.

Towards a Transdisciplinary Governance of Artificial Intelligence in Africa

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.

Embedding AI Risk Governance into Knowledge-Based Organizations: A Path Towards Systemic Sustainability

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.

Ethics and Responsibility in the Governance of Artificial Intelligence

Artificial intelligence (AI) is transforming business, government, and society at a pace that exceeds the development of governance frameworks. This article presents an academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026, examining Indonesia's strategic position in the evolving global AI landscape. The presentation argued that Indonesia should become a rule shaper rather than a rule taker by developing governance frameworks that reflect national and regional priorities instead of relying solely on external regulatory models. Responsible AI requires more than ethical principles; it depends on capable institutions, effective implementation, qualified professionals, and credible regulatory mechanisms. Key challenges identified include fragmented governance, implementation gaps, and operational risks associated with large language models, including prompt injection, hallucination, and data leakage. The presentation further emphasised the importance of independent AI assurance, certification systems, and institutional readiness to support responsible AI adoption across strategic sectors. Building digital talent and governance capability was presented as the essential foundation for reducing implementation gaps and strengthening long-term competitiveness. The presentation concluded that developing national capability while adapting international best practices provides the most appropriate pathway for enabling Indonesia to contribute to the future development of AI governance.