Aug 2026· Applied Artificial Intelligence Research· Vol 2, pp. 18-26· 0 citations· 23 references
TL;DR
It is argued that AI should be understood as a socio-technical system rather than a neutral technical upgrade and a human-centered governance framework based on educational purpose, proportionality, contestability, transparency, stakeholder participation, vendor accountability, professional development, and continuous audit is proposed.
Abstract
This article examines how artificial intelligence and big data can support decision-making in private school management while preserving educational judgment, stakeholder trust, and institutional responsibility. Private schools operate under distinctive pressures, including enrolment competition, parental choice, financial sustainability, reputational risk, and regulatory compliance. These conditions make data-driven tools attractive for admissions forecasting, learning quality monitoring, student support, teacher development, financial planning, family communication, reputation analysis, and risk management. However, the use of AI in private schools also raises significant concerns, including data privacy, algorithmic bias, excessive surveillance, metric drift, vendor dependence, and the possible substitution of professional judgment with automated recommendations. Drawing on literature from AI in education, learning analytics, privatization, and data ethics, this article argues that AI should be understood as a socio-technical system rather than a neutral technical upgrade. It proposes a human-centered governance framework based on educational purpose, proportionality, contestability, transparency, stakeholder participation, vendor accountability, professional development, and continuous audit. The article concludes that AI and big data can improve private school decision-making only when they are embedded in responsible governance practices that strengthen, rather than weaken, educational values and institutional legitimacy.
Artificial Intelligence (AI) promises major performance gains in the public sector. However, as AI is primarily procured from private suppliers, adequate governance is crucial, particularly regarding AI risk perceptions. This study links existing AI governance frameworks and make-or-buy considerations to empirically examine perceived AI benefits and risks and their effects on supplier performance.
A novel workshare governance construct is established, capturing the allocation of digital development responsibilities and is adapted to a public sector setting, connecting dynamic capabilities with individual AI-related factors like motivation and risk awareness. A partial least squares structural equation model (PLS-SEM) is applied to test hypotheses between governance, AI readiness and performance with survey data from 104 questionnaires.
The necessity of workshare governance and its role as a mitigator of negative effects is empirically validated. AI risk awareness significantly reduces supplier performance, while AI motivation shows no significant effect. The results indicate that motivation alone is insufficient; risk awareness and governance determine whether AI leads to measurable improvements in supplier performance.
The study challenges the assumption that AI adoption automatically yields transformation. It highlights workshare governance and risk awareness as potential explanatory mechanisms, suggesting the need to refine existing AI readiness and adoption models, particularly for regulated or defense-related procurement environments.
Policy makers and procurement managers should prioritize governance frameworks addressing individual barriers to AI use and rigorously assess the value of potential AI products and services.
This study provides empirical evidence for the high relevance of procurement for public sector AI services via supplier-related governance perspectives.
Max Ernst Hamscher, A. Glas, Michael Essig· International Journal of Pub...· 0 citations
Artificial Intelligence (AI) is increasingly transforming organisational decision-making processes across finance, human resource management, marketing, risk assessment, and strategic planning. While AI-driven systems offer significant benefits in terms of efficiency, accuracy, and predictive capabilities, they simultaneously raise critical ethical and governance concerns related to transparency, accountability, fairness, privacy, and stakeholder trust. As organisations increasingly rely on algorithmic decision-making, traditional corporate governance frameworks face new challenges in ensuring the responsible and socially acceptable deployment of AI.
This study systematically reviews the emerging literature on Ethical AI and Corporate Governance to examine the social implications of AI-driven decision-making in business organisations. Using a systematic literature review methodology, relevant studies published between 2018 and 2025 were identified through Scopus, Web of Science, Google Scholar, Emerald Insight, and ScienceDirect databases. Following a structured screening and selection process, 68 peer-reviewed publications and policy reports were analysed using thematic analysis.
The review identifies six major themes: algorithmic bias and discrimination, transparency and explainability, accountability and responsibility, privacy and data governance, workforce transformation, and regulatory governance frameworks. Beyond synthesising existing knowledge, the study critically examines tensions between innovation and regulation, human oversight and automation, and Western and non-Western AI governance approaches. The paper proposes an integrated Ethical AI Governance Framework and discusses practical implications for boards of directors, policymakers, and organisational leaders. The study contributes to the growing discourse on responsible AI by offering a governance-centered perspective that balances technological innovation with ethical responsibility and sustainable organisational performance
C. Dani· International journal of res...· 0 citations
Colorado's 179 K-12 public school districts operate as autonomous governance units, each responsible for securing and managing student data assets that span health, financial, residential, and academic records. The accelerating integration of artificial intelligence (AI) and machine learning (ML) tools into administrative workflows, productivity software, and instructional platforms has fundamentally altered the risk landscape for student data, yet governance frameworks at the state, district, and school levels have not kept pace. This dissertation investigates whether Colorado's decentralized educational governance structure is institutionally capable of producing equitable, secure, and sustainable data governance outcomes in the AI era.
Drawing on Institutional Theory (DiMaggio & Powell, 1983) as the primary explanatory lens, Deming’s (1986) Systems of Profound Knowledge as the operational evaluation framework, and Transformative Leadership theory (Shields, 2010, 2018) as the educational leadership framework, this study advances the argument that governance failure in Colorado’s K-12 system is not a technical problem but an institutional one. Decentralized structures, the absence of standardized equity metrics, and compliance-driven rather than outcome-driven policy cultures combine to produce a self-reinforcing cycle that systematically disadvantages rural districts and vulnerable student populations. This cycle is conceptualized as the Governance-Equity Deficit Model (GEDM), which constitutes the original theoretical contribution of this dissertation.
Chapter 1 establishes the problem statement, defines the scope of the study, and presents a single integrated research question addressed through three sequential analytical phases. Chapter 2 synthesizes existing literature through the lens of the GEDM, encompassing the historical evolution of information security paradigms, Colorado state law applicable to student data governance, equity and algorithmic accountability scholarship, and modern AI risk management frameworks including the NIST AI Risk Management Framework (AI RMF; National Institute of Standards and Technology [NIST], 2023), ISO/IEC 42001 (International Organization for Standardization & International Electrotechnical Commission [ISO/IEC], 2023), and related international standards. Chapter 3 presents a systematic policy document analysis methodology grounded in the Deming framework and augmented by a structured AI-assisted screening protocol with transparent human oversight and inter-rater validation. The study focuses exclusively on publicly available policy, legislative, and governance documents.
The findings are intended to inform the Colorado Department of Education (CDE), state legislative bodies, and district technology leadership with actionable, evidence-based recommendations for centralized coordination mechanisms, standardized equity metrics, and pathways to improve governance maturity across Colorado's diverse district landscape.
This study examines how policy silence functions as a governance mechanism in the context of generative artificial intelligence (AI) in K–12 education. While existing research has focused on how districts regulate or integrate AI, far less attention has been given to what happens when formal guidance is delayed, incomplete, or unresolved. Drawing on a single-district qualitative case study of a Texas public school district, the analysis uses interviews and document analysis as primary data sources, while student survey data provide descriptive context regarding patterns of AI access and guidance. Findings show that policy silence actively redistributes interpretive authority to educators and school leaders, shifting responsibility for ethical and instructional decision-making onto individual classrooms without corresponding institutional support. This redistribution produces uneven enactment and may contribute to disparities in student access, guidance, and learning opportunities. Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined. Equitable AI integration requires policies that pair clarity with support for professional judgment, positioning AI governance as central to contemporary school improvement.
A. Miles, Khalid H. Arar· Improving Schools· 0 citations
Artificial Intelligence (AI) is rapidly transforming governance systems worldwide by improving administrative efficiency, public service delivery, and evidence-based policymaking. While developed countries have made substantial progress in integrating AI into governance structures, many developing countries continue to face institutional, ethical, and social challenges that limit the equitable benefits of AI. This study examines the relationship between artificial intelligence, social justice, and public governance from the perspective of the Global South. Drawing upon Public Value Theory, Institutional Theory, and the Capability Approach, the study explores how AI can contribute to inclusive governance while addressing issues of inequality, transparency, accountability, and citizen participation. A quantitative cross-sectional research design is proposed using survey data collected from 300 citizens across developing countries, with Pakistan serving as the primary case study. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings suggest that responsible AI adoption strengthens public governance by improving transparency, efficiency, and service accessibility, while social justice significantly enhances citizens' trust in government institutions. The study contributes to the emerging literature on AI governance by emphasizing that technological innovation alone cannot achieve sustainable development without institutional reforms, ethical regulation, and inclusive public policies. The findings offer practical recommendations for policymakers seeking to promote equitable AI governance across the Global South.
H. Bashir, Saima Razzaq Khan, Waseem Ullah· Aposta: Revista de Ciencias...· 0 citations
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