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.
Abstract
Artificial intelligence (AI) governance has converged around transparency, accountability, safety, privacy, fairness, human rights, human oversight and risk management. However, this vocabulary remains limited when AI is understood as a sociotechnical force shaping agency, work, truth, democratic trust, meaning and long-term human futures. This article is a critical conceptual review, with a structured Scopus-based mapping of 48 peer-reviewed articles, a critical documentary analysis of six international AI governance frameworks and a supplementary interpretive engagement with Magnifica Humanitas. Using a PRISMA-informed flow diagram for transparent reporting and abductive thematic synthesis, the study identifies four fragmented literatures: AI ethics and governance; anthropological accounts of dignity, autonomy, agency and vulnerability; spiritual and theological approaches to meaning and moral formation; and civilizational analyses of democracy, war, transhumanism and existential risk. The findings do not suggest that these concerns are absent from AI ethics; rather, they show that they remain dispersed and are not consistently translated into governance criteria. This article proposes the Anthropological, Spiritual and Civilizational (ASC) Framework as a diagnostic heuristic for extending trustworthy AI toward dignity, truth, social justice and humane futures. The framework is conceptual and requires future empirical and expert validation.
The accelerating deployment of artificial intelligence (AI) across public and private life has outpaced the moral vocabularies available to evaluate it. Prevailing AI-ethics frameworks, articulated largely within Western traditions, converge on principles such as fairness, transparency, and accountability, yet remain thin on the spiritual and communal dimensions of moral judgement in Muslim societies. This study asks how maqāṣid al-sharīʿah, the higher objectives of Islamic law, may be operationalised as an evaluative instrument for AI governance rather than a declarative ideal. Employing qualitative library research, the study analyses primary Islamic sources alongside peer-reviewed scholarship from 2021 to 2026 through content analysis and philosophical-normative reasoning. The analysis maps four recurring problems (the erosion of privacy, algorithmic bias, opacity and diffuse accountability, and the attrition of human moral agency) and evaluates each against the five essential objectives (al-ḍarūriyyāt al-khams). The resulting framework translates every objective into governance criteria and correlates them with globally recognised AI principles, yielding an eleven-criterion instrument together with an ordering rationale for cases of value conflict. The study contributes an operational, value-based model that complements rather than displaces existing regulatory approaches, offering developers, regulators, and Shariah boards a design vocabulary for anticipating harm before deployment.
Maman Supardi, Hilmiy Hanif, Mursyid Rahman et al.· West Science Islamic Studies· 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
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.
Patrick Rudolf Dannacher Dannacher· Proceeding Jakarta Geopoliti...· 1 citation
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
Artificial intelligence is increasingly becoming a governing medium through which institutions classify persons, allocate opportunities, structure work, produce knowledge and mediate public trust. Current AI governance frameworks emphasise risk classification, technical assurance, transparency, accountability and human oversight. These instruments are necessary, but they remain incomplete when algorithmic decisions reshape the meaning of agency, dignity, responsibility and social recognition. This paper develops a humanities-based framework for algorithmic governance suitable for law, management and public life. Using an interdisciplinary conceptual methodology, it synthesises legal-policy frameworks, AI ethics scholarship, management studies and contemporary philosophical work on ontological instability, AI stakeholder recognition and moral responsibility. The paper argues that algorithmic governance should not be assessed only by whether systems are accurate, explainable or compliant, but also by whether affected persons retain interpretive agency, contestatory power, relational recognition and meaningful participation in institutional life. It proposes the Human Agency Impact Matrix, a six-dimensional framework that evaluates algorithmic systems through interpretability, contestability, relational accountability, dignity preservation, participatory design and institutional reversibility. The analysis shows that risk-based regulation is strongest when complemented by humanistic assessment of how AI changes roles, identities, vulnerabilities and obligations. The paper concludes that responsible AI governance must be understood as a cultural and institutional practice: a way of preserving human agency within socio-technical systems that increasingly act before, beside and sometimes instead of human judgment.
Kwan Hong Tan· International Journal of Law...· 0 citations
Agentic artificial intelligence (AI) alters the governance problem because model outputs can become multi-step actions with financial, legal, informational, and social consequences. Existing governance instruments widely endorse human oversight, transparency, accountability, and redress, yet they do not consistently specify what people must remain able to do when agency is delegated to an AI system. This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore. Provision-level coding, abductive pattern analysis, negative-case examination, and a cross-framework coverage matrix identify six themes: human-centric convergence with operational divergence; oversight without empowerment; late-stage contestability; a reversibility deficit; fragmented accountability; and temporal-capability asymmetry. The paper develops cognitive sovereignty as the practically exercisable capacity to understand, authorize, interrupt, contest, restore, and assign responsibility for consequential processes delegated to AI. It then proposes the CLEAR² framework, comprising Comprehension, Legitimate authorization, Effective intervention, Appeal and contestation, Restoration and reversibility, and Responsibility and remedy. CLEAR² integrates ex ante, runtime, and ex post controls and treats the weakest capability as a constraint on meaningful human control. The study advances AI governance theory by shifting the unit of analysis from human presence to preserved agency, while offering organizations a maturity model, lifecycle control architecture, and audit questions for responsible agentic deployment.
Kwan Hong Tan· Open Access Journal of Multi...· 0 citations