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.
Artificial intelligence (AI) adoption is accelerating across industries, introducing novel governance, risk, and compliance (GRC) challenges that traditional cybersecurity frameworks cannot fully address. Standards such as ISO/IEC 27001 and the NIST Risk Management Framework safeguard IT assets but do not comprehensively mitigate AI-specific risks like adversarial attacks, model drift, and ethical concerns such as fairness and accountability. This gap raises critical questions about how organizations can govern AI responsibly while maintaining security and compliance. 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. Through comparative analysis, we examine how these frameworks address governance structures, risk assessment methodologies, compliance mechanisms, and transparency requirements. We also explore integration strategies with existing cybersecurity and enterprise risk models. Our findings reveal a fragmented ecosystem with overlapping principles but inconsistent enforcement and technical depth. While global standards emphasize values such as transparency and accountability, operational guidance on adversarial robustness and lifecycle risk monitoring remains limited. The review identifies best practices for embedding GRC into AI development pipelines, including continuous monitoring, documentation artifacts, and risk-tiering strategies. It also highlights gaps in interoperability, audit tooling, and liability regimes. This research speaks to policymakers, compliance officers, cybersecurity professionals, and AI developers seeking harmonized governance approaches. Future work should prioritize unified audit standards, empirical evaluation of governance effectiveness, and integration of AI risk metrics into ESG reporting to ensure trustworthy and sustainable AI deployment.
Abimbola Filani, J. Opoku· Magna Scientia Advanced Rese...· 0 citations
Organizations deploying artificial intelligence systems in high-stakes domains—employment screening, credit underwriting, healthcare allocation, criminal justice—confront a critical governance challenge: how to operationalize bias mitigation across the full system lifecycle when accountability diffuses across technical, legal, and operational teams. Despite growing regulatory pressure from the EU AI Act and U.S. anti-discrimination statutes, most organizations lack integrated frameworks that translate fairness principles into daily practice. Technical research offers debiasing algorithms but assumes centralized control that rarely exists; regulatory guidance defines compliance endpoints without implementation pathways; organizational studies document failure patterns without producing adoptable solutions. This article synthesizes cross-disciplinary evidence to present a practitioner-oriented approach to lifecycle-based AI bias mitigation. Drawing on organizational governance research, technical fairness literature, and regulatory frameworks, the article maps seven critical intervention stages—from problem formulation through continuous monitoring—assigns explicit accountability at each stage, and embeds structural mechanisms that address role ambiguity, siloed decision-making, and deployment pressure. The approach provides Chief AI Officers, compliance teams, and technical leaders with concrete governance architecture grounded in real organizational constraints and regulatory obligations.
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
Artificial intelligence (AI) is increasingly integrated into organizational governance, reshaping decision-making processes, accountability mechanisms, and stakeholder relationships. This study investigates the differences between AI-assisted governance systems and traditional governance approaches regarding accountability and stakeholder trust. A narrative literature review was conducted by analyzing ten scholarly publications published between 2021 and 2026 across diverse sectors, including public administration, healthcare, finance, corporate governance, and human resource management. The review findings reveal that AI-assisted governance systems generally enhance accountability through automated auditing, explainable decision-making, predictive risk assessment, and continuous compliance monitoring. Several studies reported improvements in governance performance, ethical compliance, and risk management compared with conventional governance models. In addition, stakeholder trust tends to increase when AI systems incorporate transparency, fairness, and explainability features that allow users to understand and evaluate algorithmic decisions. Despite these advantages, important challenges remain, including unclear responsibility attribution, the lack of standardized AI governance and auditing frameworks, and potential trust erosion caused by excessive dependence on automated systems. The effectiveness of AI-assisted governance is also influenced by organizational context, leadership commitment, governance maturity, and the extent of human oversight. 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. These findings contribute to the ongoing discussion of responsible AI governance and provide practical insights for organizations pursuing governance innovation.
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
Artificial Intelligence (AI) is transforming Nigeria's digital landscape, driving operational efficiency in sectors such as fintech, healthcare, and telecommunications. These AI systems rely heavily on large-scale processing of personal data to automate decisions, ranging from loan approvals to employment screening. While the Nigeria Data Protection Act (NDPA) 2023 represents a milestone in aligning Nigeria with global privacy standards, the inherent nature of AI—characterized by "black-box" decision-making and the potential to amplify existing social inequalities through biased training data—presents novel challenges. The core issue facing Nigerian data subjects is whether the existing data protection framework can prevent "AI-driven errors" and discriminatory outcomes. Despite the passage of the NDPA 2023, substantial enforcement and policy gaps remain concerning; AI-driven decision-making and algorithmic bias. The NDPA operates primarily as a privacy law rather than specialized AI-regulation Act, leading to several critical deficiencies like algorithmic opacity; unaccountable bias; limitations of human in the loop and enforcement challenges. The research adopted a doctrinal research methodology. It involves a critical analysis of the primary legislation and relevant case law. A comparative analysis is drawn with international frameworks, such as the European Union General Data Protection Regulation 2018, to evaluate the sufficiency of Nigeria's approach. The research found that there is absence of fair metrics; lack of explainable AI Standards; contextual inaccuracy and high risk processing ambiguity. It therefore recommended for legislative review, mandatory bias audit and enhanced transparency. The research concluded that the Nigeria Data Protection Act 2023 is a necessary but insufficient tool for managing the risks associated with AI-driven decision-making.
Chinenye JOY MGBEOKWERE, Marvelous INI-OBONG MONDAY· Abuja Journal of Public Law· 0 citations
The rapid integration of artificial intelligence (AI) into enterprise decision-making systems has fundamentally transformed organizational governance across sectors, enabling automated decisions in credit assessment, healthcare resource allocation, workforce management, pricing strategies, and public-sector services. As AI increasingly influences decisions with significant social and economic consequences, the need for robust governance mechanisms has become as important as technological innovation itself. However, governance frameworks, accountability mechanisms, and equity assessment practices have not advanced at the same pace as AI deployment, creating substantial risks related to transparency, fairness, regulatory compliance, and organizational trust. 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. Drawing upon implementation experiences and governance practices across telecommunications, financial services, and healthcare, the study synthesizes evidence from engineering, policy, ethics, and critical social science literature to develop a comprehensive perspective on responsible AI architecture. The analysis demonstrates that effective AI governance requires integrating technical controls with organizational accountability, continuous monitoring, auditability, risk management, and human oversight throughout the AI lifecycle. Furthermore, the study argues that technical governance alone cannot eliminate algorithmic bias or inequitable outcomes unless accompanied by structural policy interventions addressing the underlying institutional and societal conditions embedded within training data and decision processes. The proposed governance perspective positions responsible AI as a foundational engineering discipline that enhances regulatory compliance, organizational resilience, stakeholder trust, and long-term business sustainability while reducing legal, operational, and reputational risks. The findings provide practical guidance for enterprises seeking to modernize AI-enabled decision systems through governance architectures that balance innovation with accountability, ethical responsibility, transparency, and equitable value creation across increasingly complex digital ecosystems
M. Modi· International Journal of Eng...· 0 citations