Developing an Algorithmic Accountability and Data Sovereignty Framework for the governance of Artificial Intelligence platforms in United States education
This study showed how current policy architectures may permit the commercial use of student data and suggests immediate policy changes that would guarantee control of student data, ensure algorithmic accountability, and also prioritize the benefits of students over data monetization.
Abstract
The paper analyzed regulations, standards, and guidelines on the use of artificial intelligence learning platforms in the United States by conducting a systematic review of documents and legal analysis. The data were gathered by purposely sampling 47 documents, including privacy policies, federal and state legislative documents and reported case studies. The analysis identifies recurring themes, including inadequate consent and transparency mechanisms, legal uncertainty across jurisdictions, and documented risks related to algorithmic decision-making. The document analysis suggests that FERPA and COPPA appear insufficiently adapted to address the data processing capabilities of contemporary Artificial Intelligence platforms, which point to meaningful gaps in student privacy protection. Analysis of reported data shows many schools were using Artificial Intelligence with inadequate district oversight, while 60% of principals and 25% of teachers were currently using AI, although only 18% of districts had developed formal guidelines about its usage. Using Data ethics, technological determinism and surveillance capitalism as analytical lenses, this study showed how current policy architectures may permit the commercial use of student data. Reported Case materials suggest risk of algorithmic bias, discriminatory disciplinary actions, and expanded surveillance practice. The study suggests immediate policy changes that would guarantee control of student data, ensure algorithmic accountability, and also prioritize the benefits of students over data monetization.
The research concludes that banking regulatory compliance in the digital era cannot be achieved through passive adherence to legacy frameworks; it requires a proactive, sociotechnical approach to algorithmic transparency.
Joseph Kikomeko, Augustine Alloysius Ogbe· Journal of Banking and Finan...· 0 citations
Public-sector adoption of artificial intelligence (AI) is accelerating, yet governmental use of AI unfolds within decision environments structured by legality, procedural fairness, privacy, equality, ministerial accountability, and democratic scrutiny. his review advances the concept of constitutional intelligence as a governance architecture for trustworthy public-sector AI. Using a narrative-comparative review of authoritative policy instruments, legal frameworks, and peer-reviewed scholarship from Canada, the United Kingdom, Europe, and major international standard-setting bodies, the article identifies a convergent set of design expectations for public AI: rights-sensitive scoping, ex ante impact assessment, documentation and traceability, human oversight, proactive transparency, and continuous institutional review. The analysis further shows that responsible AI adoption in government depends not only on model-level controls but also on the quality of data stewardship, interoperability, and administrative continuity. These findings are synthesized into a constitutional-intelligence model suited to Westminsterstyle institutions and other public administrations that must justify automated action under conditions of legal contestability and public accountability. The central claim is that constitutional and democratic requirements should not be treated as external compliance burdens. When embedded into institutional design, they operate as productive constraints that improve legitimacy, implementation discipline, and the long-term trustworthiness of AI-enabled public decision-making.
C. Oliveira· Open Access Journal of Data...· 0 citations
The rapid growth of artificial intelligence, big data, and digital technologies has significantly transformed governance systems worldwide. Algorithmic governance refers to the use of automated and data-driven systems in public decision-making, administration, surveillance, and policy implementation. This paper examines whether algorithmic governance poses a threat to democracy, creates opportunities for democratic innovation, or represents a broader transformation of democratic practice. The methodology of this study is based on qualitative and comparative approach, drawing largely on secondary sources such as recent peer-reviewed journal articles, books, government reports and policy briefings and publications from reputed national and international organizations. Drawing on perspectives from political sociology, governance studies, and digital democracy, the study argues that algorithmic systems are not politically neutral. They often reproduce social inequalities, reduce transparency, and concentrate power within states and large technology corporations. The increasing use of surveillance technologies, predictive algorithms, and digital platforms raises concerns regarding privacy, accountability, misinformation, and citizen autonomy. However, the paper also highlights the positive potential of algorithmic governance in improving public service delivery, enhancing citizen participation, strengthening evidence-based policymaking, and promoting digital inclusion. Emerging forms of e-governance and participatory digital platforms demonstrate that technological systems can support democratic engagement when guided by ethical principles and institutional accountability. The study concludes that algorithmic governance should not be understood solely as a threat or an opportunity, but as an ongoing democratic transformation that demands stronger regulation, transparency, digital rights protection, and inclusive governance frameworks.
Deepak Pun Magar, Krishna Prasad Chaudhary· Intellectual Inception: A Mu...· 0 citations
This study examined how artificial intelligence (AI) can enhance accountability and transparency in South African municipalities. The study responded to persistent municipal governance challenges, including weak financial oversight, fragmented data systems, service delivery inefficiencies and declining public trust. A systematic review design was adopted to synthesise global, African and South African evidence on AI adoption in public administration and municipal governance. The review was guided by PRISMA principles and included 43 eligible studies selected from academic, policy and institutional sources. The Technology Acceptance Model and Institutional Theory were used to interpret both user-level and institutional factors influencing AI adoption, including perceived usefulness, perceived ease of use, staff readiness, organisational resistance, regulatory pressure and governance norms. The findings show that AI can support municipal accountability through fraud detection, procurement monitoring, audit support and anomaly identification. AI can also improve transparency through citizen-service chatbots, complaint-routing systems, open data tools and integrated information platforms. However, the review found that AI adoption in South African municipalities is constrained by poor digital infrastructure, weak data interoperability, limited technical skills, financial pressure, organisational resistance and unclear ethical governance arrangements. The study concludes that AI should be treated as a governance support tool rather than a complete solution to municipal failure. Its value depends on phased implementation, data modernisation, capacity building, legal safeguards, human oversight and inclusive citizen engagement. Future research should examine municipal-level AI implementation, public trust, algorithmic accountability and the effects of AI on equitable service delivery.
S. Nokele, Khathutshelo Matshela· Journal of Cultural Analysis...· 0 citations
The rapid development of Artificial Intelligence (AI) has significantly transformed public governance, public services, and the digital ecosystem. However, such advancement has not been accompanied by the establishment of a comprehensive administrative legal framework in Indonesia. AI-related regulations remain fragmented across sectoral legal regimes, resulting in regulatory inconsistency, unclear supervisory authority, weak auditing mechanisms, and limited administrative accountability in AI deployment. This research aims to analyze the weaknesses of AI governance in Indonesia and reconstruct an administrative supervision model of AI from the perspective of Administrative Law, considering global regulatory practices. This study employs normative legal research using statutory, conceptual, and comparative approaches. The findings indicate that Indonesia has not yet established an integrated administrative governance framework for AI, particularly regarding regulatory authority, administrative discretion, algorithmic audits, certification, compliance oversight, and accountability of administrative bodies or public officials. Drawing from the European Union Artificial Intelligence Act, OECD AI Principles, and UNESCO Recommendation on the Ethics of Artificial Intelligence, this study proposes a reconstructed administrative governance model based on risk-based supervision, strengthened regulatory authority, compliance auditing, certification mechanisms, and institutional accountability. This research emphasizes that AI governance should be positioned as part of digital administrative governance and developed as a domain of Administrative Law to ensure legality, public interest protection, and legal certainty in the digital transformation era.
Yudistira Nugroho, S. Antari· Mendapo: Journal of Administ...· 0 citations
South African and Nigerian law guarantees rights to privacy and due process, but national security carve-outs and proprietary artificial intelligence (AI) systems shielded from regulatory scrutiny can make those protections difficult to enforce. As African governments progressively rely on foreign vendors for AI-enabled surveillance technologies, a central question is how constitutional protections can be meaningfully enforced when the systems governing surveillance remain technically and legally inaccessible to domestic oversight. The theoretical contribution is the concept of the algorithmic sovereignty gap, which names the persistent disjunction between constitutional guarantees and the operational realities of vendor-controlled tools. Drawing on qualitative content analysis of constitutional law, statutes, case law, and investigative reporting in both countries, and extending Pasquale's black-box problem and Couldry and Mejias's data colonialism framework through a postcolonial lens, the article shows how procurement-led deployment and security exceptions hollow out constitutional rights that the legal texts appear to guarantee. The prescriptive contribution is a hybrid governance model that closes this gap by anchoring transparency and accountability in constitutional baselines, supported by mandatory AI impact assessments, audit rights, source-code escrow, data localisation, and procurement transparency.
Maria Lungu· Potchefstroom Electronic Law...· 0 citations