Jul 2026· International Journal of Health Governance· 0 citations· 8 references
Abstract
This Viewpoint argues that prevailing ethics-based and compliance-oriented approaches to artificial intelligence (AI) in health are insufficient for the dynamic, context-dependent realities of contemporary AI systems. It proposes a shift toward collaborative stewardship, a model that emphasizes shared responsibility, continuous learning and meaningful stakeholder participation across the full lifecycle of AI in health.
The analysis draws on a structured synthesis of peer-reviewed studies, major international policy documents and interdisciplinary scholarship published between 2021 and 2025. Using this evidence base, the paper introduces the C-STEER framework, which outlines practical components of collaborative stewardship and maps them to key stages of the AI lifecycle.
The synthesis reveals that static ethical principles and top-down regulatory models frequently fail to account for real-world variability, equity concerns and the evolving behavior of systems. Governance approaches that combine legal, technical, organizational and participatory mechanisms, supported by continuous monitoring and local adaptation, are better positioned to build trust, enhance accountability and promote equitable outcomes.
By defining collaborative stewardship and presenting the C-STEER framework, this Viewpoint moves beyond compliance-driven governance and offers a practical, context-responsive model for responsible AI integration in health systems.
The exponential adoption of artificial intelligence (AI), worsening climate disruptions, and new One Health approaches to global health policy, prompt research organizations to re-examine their responsibilities. AI offers powerful capabilities from precision medicine to ecosystem monitoring. Yet its deployment raises concerns related to high energy and water demands, hardware that depends on scarce resources, data governance, ethics and equity. There is further risk that technological solutions may distract from essential ecological and social action. A One Health lens, recognizing the interconnectedness of human, animal, and environmental health, encourages organizations to examine whether their AI tools and infrastructures truly support healthy ecosystems and communities. This raises critical questions: How should organizations balance scientific urgency with the responsibility to protect people, places, and data? How can meaningful interdisciplinary collaboration be structured? How can institutions uphold accountability to land, water, Indigenous communities, and future generations when deploying AI? Operational issues are central to this conversation. This moderated panel will explore how data-intensive research organizations can balance innovation with stewardship. Discussion topics include: What constitutes climate-resilient and environmentally responsible research infrastructure? What is the purpose of calculating organizational carbon footprints, and how can institutions meaningfully measure and manage the carbon and material impacts of AI? How can research cultures encourage the use of energy-efficient models, sustainable computational practices, and responsible procurement? Input from this panel will inform recommendations that help data-intensive organizations reimagine their responsibilities not only as the producers of knowledge but as institutions whose everyday operations shape a sustainable and ethical future.
Magda Nunes de Melo, Charles Victor, Kim Mcgrail et al.· International Journal of Pop...· 0 citations
An academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026 is presented, examining Indonesia's strategic position in the evolving global AI landscape.
Patrick Rudolf Dannacher Dannacher· Proceeding Jakarta Geopoliti...· 1 citation
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
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 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
Concerns regarding ethical issues, such as algorithmic fairness, accountability, transparency, and the preservation of human dignity, have increased due to the fast integration of artificial intelligence (AI) into a variety of global sectors. Principles like safety, explainability, and regulatory compliance are given top priority in many of the current global AI governance frameworks. The sociocultural, historical, and communal dynamics that are common in African communities may not be sufficiently accommodated by these methods, which are frequently drawn from Western intellectual traditions. The potential of situational ethics, specifically, Joseph Fletcher's agape-focused framework, as a further lens for evaluating AI systems in African contexts is examined in this research. Employing the hermeneutic method of inquiry, the topic illustrates how situational ethics’ fundamental components: pragmatism, relativism, positivism, and personalism, can promote context-sensitive assessments of AI through conceptual analysis. This viewpoint emphasizes human and communal well-being over rigid regulations, placing agape (selfless, other-oriented love) as the paramount norm. It provides a way to align AI deployment with African relational and communitarian values, such as those embodied in the Ubuntu philosophy of relationality. This paper suggests that Fletcher’s agapeic framework may solve epistemic inequities in global AI discourse and fosters inclusive technology growth by fusing situational ethics with traditional African philosophies.
Oviemuno Egara, Bridget Oviemuno, Charles Elijah· Mathematics and Computer Sci...· 0 citations