This study proposes a three-layered governance framework artifact that operationalizes ethical AI principles—fairness, transparency, accountability, privacy, and human oversight—within AI-enabled IAM systems.
A normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation suggests that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance.
A Multi-Layer Social-Theoretical AI Ethics Framework (MLST-AEF) that integrates normative ethical reasoning, stakeholder analysis, institutional context, bias and power assessment, and structured decision support is developed.
M. Fakrudeen, J. Otieno· AI and Ethics· 0 citations
A novel, unified governance framework centred on digital trust is proposed that distinctly integrates the AI Trust Framework and Maturity Model (AI TMM), the Tiered Ethical Cybersecurity Model (TECM), and privacy preserving technologies such as federated learning to operationalize ethics by design.
Muhammad Faris bin Nordin, Muhammad Din bin Khalid, Normal Mat Jusoh· International journal of res...· 0 citations
There is a growing use of Artificial Intelligence (AI) systems in high-stakes environments such as healthcare and transportation, where errors or misuse can lead to serious and potentially irreversible consequences. Existing regulatory frameworks remain fragmented and often inadequate to address the complexity of AI development in these settings. Although ethical principles such as safety, fairness, accountability, transparency and human oversight are widely recognised, their translation into enforceable legal obligations remains uneven across jurisdictions and sectors. The central question addressed in this paper is how AI use in high-stakes environments should be regulated to achieve effective and enforceable obligations. The paper makes four contributions. First, it develops a three-tier Ethics Pyramid, a framework that distinguishes foundational ethical values, second-level governance principles, and third-level operational mechanisms. The framework functions as an allocation model, helping regulators identify which ethical principles should be juridified and at what level of governance this should occur. Second, the paper evaluates regulatory theories, strategies and instruments and argues that a polycentric, multi-layered legal framework is better suited to high-stakes AI governance. Third, it conducts a comparative analysis of high-stakes AI regulation in the UK, the EU, the US and China, alongside a sectoral examination of healthcare, autonomous vehicles, criminal justice and public sector decision-making. The analysis reveals significant variation in regulatory maturity. Fourth, it develops a polycentric, multi-layered legal architecture illustrated through case studies of AI use in UK prisons and autonomous vehicles, highlighting the contrast between fragmented and mature regulatory regimes.
J. Mante, Blessing Abeji, H. Kalutarage et al.· AI and Ethics· 0 citations
The central claim is that constitutional and democratic requirements should not be treated as external compliance burdens when embedded into institutional design, they operate as productive constraints that improve legitimacy, implementation discipline, and the long-term trustworthiness of AI-enabled public decision-making.
C. Oliveira· Open Access Journal of Data...· 0 citations
An exploratory, expert-informed Human-Centred AI (HCAI) pre-design governance framework that translates selected risk-based obligations of the EU Artificial Intelligence Act into early organisational decisions about human oversight, data accountability, documentation, and bounded algorithmic autonomy is developed.
Hyun-Kyung Lee, Cheolhee Yoon, B. Lee· Syst.· 0 citations
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