Jul 2026· Journal of Economic, Finance Research and Review· 0 citations
TL;DR
It is concluded that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.
Abstract
This study examines data governance, bias mitigation, and legal risk within the context of a holistic Artificial Intelligence (AI) compliance framework for US companies operating in high-stakes sectors such as healthcare, finance, insurance, and critical infrastructure. The rapid adoption of AI systems has improved efficiency and decision-making capabilities. However, it has also introduced significant challenges related to algorithmic bias, lack of transparency, weak data governance, and increasing legal and regulatory exposure. Drawing on existing literature, the study highlights that inadequate governance structures and poor-quality datasets contribute to discriminatory outcomes, reduced accountability, and heightened compliance risks under evolving regulatory regimes. It further shows that algorithmic bias persists due to historical data inequalities and opaque machine-learning models, while legal frameworks such as privacy and anti-discrimination laws place additional obligations on organizations deploying AI systems. The study concludes that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.
This paper reviews emerging AI-GRC frameworks and regulations, including the OECD AI Principles, ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act, alongside industry standards from Microsoft, Google, and IBM, to reveal a fragmented ecosystem with overlapping principles but inconsistent enforcement and technical depth.
Abimbola Filani, J. Opoku· Magna Scientia Advanced Rese...· 0 citations
Drawing on organizational governance research, technical fairness literature, and regulatory frameworks, the article maps seven critical intervention stages and assigns explicit accountability at each stage, and embeds structural mechanisms that address role ambiguity, siloed decision-making, and deployment pressure.
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
Overall, AI-assisted governance offers substantial potential to strengthen accountability and stakeholder trust when supported by robust ethical safeguards, transparency measures, and clearly defined responsibility structures.
M. Mar, Ing. Nikolai Fabian Sebastián Yucra Añazco, Delia Nieves Coaquira Pari· Journal of Organizational an...· 0 citations
Artificial intelligence is increasingly embedded in critical digital infrastructure across emerging markets, supporting applications in telecommunications, financial inclusion, healthcare, agriculture, fraud detection and public service delivery. While global responsible AI frameworks have established widely accepted principles for fairness, transparency, accountability, and human oversight, translating these principles into operational safeguards remains uneven, particularly in contexts characterized by institutional fragmentation, evolving regulatory regimes, vendor dependence, and constrained governance capacity. This paper examines structural implementation gaps that arise when globally flexible responsible AI frameworks are applied without contextual integration. It advances a governance by design model that operationalizes trust through measurable proportional risk tiering, lifecycle-based oversight, and structured ecosystem accountability. Governance controls are embedded at decision points spanning design, procurement, deployment, and continuous monitoring, reducing governance debt and strengthening institutional resilience. An applied case illustration demonstrates how proportional governance can be integrated into high impact deployments while preserving scalability. Aligned with emerging international standards, including ISO/IEC 42001, the models offer a scalable, context-aware pathway for implementing trustworthy, inclusive and sustainable AI under real world constraints.
Derick Ohmar, Adil· 2026 ITU Kaleidoscope - AI a...· 0 citations
The research found that the Nigeria Data Protection Act 2023 is a necessary but insufficient tool for managing the risks associated with AI-driven decision-making and there is absence of fair metrics; lack of explainable AI Standards; contextual inaccuracy and high risk processing ambiguity.
Chinenye JOY MGBEOKWERE, Marvelous INI-OBONG MONDAY· Abuja Journal of Public Law· 0 citations
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations
