Aug 2026· Journal of Organizational and Human Resource Development Strategies· 0 citations· 23 references
TL;DR
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.
Abstract
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.
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
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.
Joy Oluchi Nwachukwu, Thaddaeuse Odhiambo, Dorcas Akorkor Apaflo et al.· International Journal of Man...· 0 citations
The widespread application of artificial intelligence (AI) in corporate resource planning and public decision-making has provided impetus for improving management efficiency and creating social value. However, the complex structure and opacity of algorithms have led to a crisis of trust, posing challenges to traditional public management accountability mechanisms. Drawing on socio-technical systems theory, this paper provides a normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation. The findings suggest that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance. Although the EU AI Act proposes a preliminary form of collaborative governance, it still has shortcomings in terms of procedural justice and the feasibility of human oversight. The governance logic should shift from individual oversight to an organization-in-the-loop approach, to achieve sustainable and responsible AI governance through the construction of a procedural justice framework.
With the growing use of artificial intelligence (AI) in public governance, understanding public willingness to delegate decision-making authority to algorithmic systems has become a key issue. While prior research has examined the relationship between trust in public institutions and trust in AI, the role of institutional trust in shaping willingness to delegate high-stakes decisions to AI remains understudied. This study aims to address this gap using nationally representative survey data from Wave 152 of the Pew Research Center’s American Trends Panel (August 2024, n = 5,410).
The study uses weighted logistic regression to assess whether confidence in the US federal government’s ability to effectively regulate AI predicts citizens’ willingness to entrust AI with important decision-making responsibilities. The analysis is based on 2,940 valid responses after excluding non-substantive answers.
The findings demonstrate that institutional trust is a statistically significant predictor of support for algorithmic delegation. Higher levels of confidence in governmental AI regulation were associated with substantially higher odds of supporting the delegation of important decisions to AI systems (OR = 1.33; 95% CI [1.19, 1.50]; p < 0.001). Although utilitarian evaluations of personal benefit exert the strongest influence, institutional trust remains significant even after controlling for sociodemographic, informational, affective factors and political predispositions.
The cross-sectional design and reliance on self-reported measures limit causal inference. The dependent variable captures normative willingness to delegate rather than the observed behavior, which is appropriate given that institutional-level AI use in higher domains is still emerging. Nevertheless, the use of national survey weights and extensive controls enhances the robustness of the findings. The results contribute to the literature on digital governance by identifying institutional trust as an independent legitimacy mechanism in the acceptance of algorithmic authority.
For policymakers, the findings suggest that public support for AI-driven governance depends not only on the performance or perceived benefits of AI systems but also on citizens’ confidence in governmental regulatory capacity. Given that AI awareness was independently associated with higher support for delegation (OR = 1.36), strengthening institutional transparency, regulatory credibility and public AI literacy may be essential for sustainable AI implementation.
As governments increasingly rely on algorithmic systems in high-stakes domains, the findings suggest that institutional trust may be an important condition for the democratic legitimacy and public acceptance of digital transformations.
This study advances research on AI governance by empirically demonstrating that institutional trust in regulatory competence functions as an independent political condition for delegating authority to algorithmic systems. Unlike prior work that examines institutional trust as one predictor among many or that measures cross-national trust differences without testing the delegation pathway, this paper theorizes institutional regulatory trust as the central legitimacy mechanism and uses normative willingness to delegate, rather than abstract approval, as the outcome.
Akniyet Nugmanova, B. Gabdulina· Transforming Government: Peo...· 0 citations
This structured literature review explores the role of corporate governance in enhancing the effectiveness of management control systems (MCS), with a particular focus on the emerging challenges and strategic directions in implementation. Drawing on peer-reviewed publications from 2020 to 2025, the study synthesizes four critical themes: (1) the functional role of governance in MCS design, (2) multidimensional indicators of control system effectiveness, (3) barriers to governance-aligned MCS implementation, and (4) strategic responses to contemporary governance demands. The review reveals that corporate governance improves MCS by reinforcing transparency, accountability, risk mitigation, and stakeholder engagement, particularly when supported by internal audit, board independence, and digital capabilities. It also highlights emerging concerns such as cybersecurity, remote work productivity, technological adaptation, and employee well-being. Theoretically, this study contributes by integrating control systems theory with digital governance and behavioral insights; practically, it offers guidance for designing responsive, ethical, and performance-driven control architectures. Limitations include the reliance on secondary sources and the lack of empirical generalization, prompting future research to investigate causal mechanisms through mixed-methods approaches
Amalia Sholehah· Jurnal Profesi Dan Manajemen...· 0 citations
Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations