A multi-layer social-theoretical framework for AI ethics

Agape and Algorithms: Reframing Artificial Intelligence Ethics in Africa Through Fletcherian Situational Ethics

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.

Governance Framework for Ethical AI in Identity and Access Governance Framework for Ethical AI in Identity and Access Management Management

Artificial Intelligence Ethics through the Lens of Maqāṣid al-Sharīʿah: Constructing a Normative Framework for Civilized Technology Governance

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.

Ethical horizons in generative AI: addressing bias and advancing fairness through responsible frameworks

This study aims to explore the ethical dilemmas posed by generative artificial intelligence (AI) and frames a responsible development framework for AI. It studies influential factors in ethical AI development. The primary study further discusses concerns about data privacy, autonomy and accountability in the context of generative AI systems. The suggested framework then relies on gray influence analysis (GINA) to assess the degree of influence, which is essential to ethical AI development. It identifies eight key factors, which include transparency, accountability and human bias, as important for ethical AI development. The framework focuses on interventions through GINA to reduce bias and achieve equal AI systems. This study shows that the most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility.” In contrast, the “Automation and replacement” factor was ranked the least influential. These factors are systematically considered, with stakeholders developing strategies to facilitate ethical AI development and societal welfare. Future research directions would include the necessary competencies and resources for controlling generative AI, studies of biases in training data sets and the identification of optimal contexts for the deployment of generative AI systems. This study uniquely explores and identifies influential factors in ethical AI development to address bias and advance fairness.

Ethical Implications of AI-Driven Decision Making in Society

AI has quickly ceased being a supporting computational device and has become a central decision-maker in various spheres of society, such as health care, finances, criminal justice, and government, education, and employment. Decision-making systems based on AI have an increasing impact on the outcomes that have significant ethical, legal, and social implications on society and individuals. Although these systems have efficacy, scalability, and objectiveness, they also introduce some essential ethical dilemmas associated with bias, fairness, transparency, accountability, privacy, autonomy, and social justice. The paper will be a systematic review of the ethical issues of AI-controlled decision-making in the society. The investigation is an interdisciplinary synthesis of the literature in the domain of computer science, philosophy, law, and social sciences in order to distinguish the major ethical hazards and novel normative structures. It analyzes the origin of algorithmic bias in data, structural model design and institutional conditions and how explainability and trust are endangered through the lack of transparency in complex machine learning models. Specific focus is made on the asymmetries of power that the AI implementation causes in which automated systems impact vulnerable and marginalized people unequally. It is suggested in the paper that a methodology of ethical governance based on principles of responsible AI should be structured, fairness-by-design, transparency, human-in-the-loop oversight, and constant impact assessment. The conceptual model of ethical risk assessment is presented to consider AI systems throughout its lifecycle, including data collection and after-deployment language. The findings underscore the fact that AI systems have ethical failures that are seldom technical but rather socio-technical, which necessitate interventions at the policy, organizational governance, and technical design levels. The paper highlights the necessity of ethical norms that are enforceable, interdisciplinary cooperation, and international harmonization of regulations to make sure that the decisions made by AI could be consistent with the basic human values. The paper ends by identifying the future research directions/decision-making, as well as determining the policy implications of transforming AI systems into trustworthy, accountable, and socially beneficial systems.

Rethinking AI ethics as a temporal socio-technical system: a critical synthesis