Analysis of systems-level ethical AI compliance architecture for U.S. corporations: Integrating governance, risk management, and automated accountability
Jul 2026· International Journal of Management & Entrepreneurship Research· Vol 8, pp. 521-530· 0 citations
TL;DR
The study found that integrated ethical AI compliance architectures are critical for innovation in the responsible application of cutting-edge technologies, organizational sustainability, stakeholder trust, and long-term corporate resilience as businesses operate in an increasingly technology-driven environment.
Abstract
The purpose of this study is to analyze the systems-level ethical AI compliance architecture for corporations in the US by integrating governance frameworks, risk management systems, automated accountability mechanisms, and legal alignment strategies. This study examines the rapid transformation of the ways that corporations operate due to these AI technologies, concurrent with ethical, legal, and operational challenges involving algorithmic bias, privacy breaches, cybersecurity risk, and transparency. Results showed that various governance models, including the National Institute of Standards and Technology AI Risk Management Framework (AI RMF), enhance organizational accountability, transparency, and regulatory compliance. Compliance-by-design measures, explainable AI systems, and automated auditing technologies also strengthen AI governance and regulatory adherence. The study also found that integrated ethical AI compliance architectures are critical for innovation in the responsible application of cutting-edge technologies, organizational sustainability, stakeholder trust, and long-term corporate resilience as businesses operate in an increasingly technology-driven environment.
Keywords: Artificial Intelligence Governance, Ethical AI Compliance, Risk Management, Automated Accountability, Legal Alignment.
The application of artificial intelligence (AI) technologies in the public sector has led to improved public services, enhanced administrative performance, and strengthened automated decision-making. However, the increasing reliance on AI systems has raised concerns regarding accountability, ethical compliance, privacy protection, transparency, human oversight, and risk management. This study, employing both conceptual and qualitative research methodologies, examines the governance factors and requirements for responsible AI implementation in the public sector. The research methodology includes a comparative analysis of international AI governance frameworks and regulations. The study identifies key dimensions influencing responsible AI implementation, such as accountability, human oversight, ethical governance, legal compliance, risk management, and transparency. The findings demonstrate that the adoption of responsible AI cannot be achieved through technological means alone but also requires a commitment to comprehensive governance mechanisms. Furthermore, the sequential interaction and interdependence of governance factors reduce operational and societal risks, increase transparency and explainability, and foster public trust in the systems. This study contributes to enriching the culture and knowledge of AI governance, and the proposed framework helps government sector leaders develop responsible AI governance in accordance with international standards and regulations.
Ghazwan Hani Hussein, Faiza Mohamed, A. Abuzreda· Journal of Technology and Sy...· 0 citations
Background: The rapid adoption of artificial intelligence (AI) in human resource management has transformed recruitment, employee evaluation, workforce analytics, and decision-making. However, the growing use of AI also introduces ethical concerns, algorithmic bias, privacy risks, accountability challenges, and increasing regulatory obligations across global organizations. Objective: This study examines the emerging role of AI-driven HR governance and explores how AI ethics, organizational risk management, and regulatory compliance can be integrated into a comprehensive governance framework for global organizations. Review Methodology: The study adopts a structured review methodology, synthesizing relevant scholarly literature, AI governance frameworks, HR management research, ethical principles, and regulatory perspectives. The review identifies major themes, governance mechanisms, risk factors, and compliance challenges associated with AI-enabled HR practices. Key Findings: The review indicates that effective AI-driven HR governance requires transparent algorithms, human oversight, ethical accountability, bias mitigation, data protection, continuous risk assessment, and alignment with evolving regulatory requirements. An integrated governance approach can improve organizational trust, responsible innovation, and regulatory preparedness. Conclusion: AI governance should become a strategic component of modern HR management. Integrating ethics, risk management, and regulatory compliance can help global organizations develop responsible, transparent, and sustainable AI-enabled HR systems.
Jaganathan Balaji· International Journal of Inn...· 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
The rapid diffusion of artificial intelligence (AI) across organisational and societal settings has heightened concerns about accountability, transparency, and ethical oversight. Existing governance mechanisms, including regulation and principle-based ethics frameworks, often struggle to address the scale, opacity, and socio-technical complexity of AI systems. In response, auditing has increasingly been proposed as a means of implementing accountability by translating ethical and legal expectations into structured oversight practices. The study employs a structured literature review methodology, analysing 71 peer-reviewed articles published between 2020 and 2025, retrieved from Scopus, Web of Science, and ProQuest. Through thematic synthesis, the review shows that AI auditing has evolved beyond technical verification towards a socio-technical governance infrastructure grounded in transparency, independence, ethics integration, and professionalisation. However, its effectiveness is constrained by persistent challenges, including algorithmic opacity, regulatory lag, fragmented standards, capability gaps, and risks of symbolic compliance. The study positions auditing as both a central tool and a critical institutional challenge within AI governance, offering insights for scholars, regulators, and practitioners seeking durable accountability mechanisms for responsible AI.
The rapid adoption of Generative Artificial Intelligence in organizational information systems has created new opportunities for improving productivity, decision-making, service innovation, and knowledge management. However, its implementation also introduces critical risks related to data privacy, information security, inaccurate outputs, algorithmic bias, ethical misuse, and declining user trust. Objective: This study aims to develop a conceptual model of Generative AI risk governance by integrating AI governance readiness, information security control, ethical AI awareness, user digital trust, and AI adoption effectiveness. The model is proposed to explain how organizations can adopt Generative AI in a secure, ethical, responsible, and trusted manner. Methodology: This study employed a conceptual research design using an integrative literature review approach. Data were collected from secondary academic sources, including peer-reviewed journal articles, reputable conference proceedings, and official technical reports relevant to Generative AI, information systems, cybersecurity, AI ethics, responsible AI governance, and digital trust. The data were analyzed through thematic synthesis to identify conceptual domains, relationships among constructs, and research propositions. Findings: The findings indicate that AI governance readiness serves as a foundational construct that strengthens information security control and ethical AI awareness. These two mechanisms contribute to user digital trust, which subsequently supports the effectiveness of Generative AI adoption in organizational information systems. Implications: This study implies that organizations should not adopt Generative AI solely based on technological benefits. Organizations need to establish governance policies, security controls, ethical guidelines, user education, and trust-building strategies to ensure that Generative AI implementation is safe, accountable, and aligned with organizational objectives. Originality: The originality of this study lies in its integrated conceptual framework, which connects technology adoption, information security, AI ethics, responsible AI governance, and digital trust into a single model for responsible Generative AI implementation in organizational information systems.
Nurdiyanto Yusuf· International Journal for Sc...· 0 citations
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.
