Governing AI: A Narrative Review of Algorithmic Accountability and Legal Frameworks
DOI:
https://doi.org/10.61978/legalis.v3i2.810Keywords:
Algorithmic Accountability, AI Regulation, Legal Frameworks, Ethical AI, Transparency, Explainability, Human RightsAbstract
The rapid integration of artificial intelligence (AI) into legal systems has sparked critical debates regarding algorithmic accountability and the adequacy of current regulatory frameworks. This narrative review aims to examine the intersection of AI and law, focusing on global responses to the ethical, legal, and social challenges posed by AI-driven decision-making systems. Utilizing a comprehensive literature search through databases such as Scopus, Google Scholar, and PubMed, key studies were identified based on relevance to AI ethics, algorithmic bias, legal governance, and data privacy. Inclusion criteria emphasized empirical and conceptual works related to legal and ethical dimensions of AI regulation. The findings reveal significant disparities in the regulatory readiness of nations, with developed countries often employing a combination of hard and soft law to enhance accountability, while developing regions struggle with infrastructural and institutional constraints. Case studies from the United States, European Union, and Southeast Asia illustrate contrasting approaches and outcomes. Central themes emerging from the literature include the need for transparency, explainability, and human rights-based governance. The review highlights systemic barriers, such as inflexible legal systems and limited stakeholder engagement, that hinder effective regulation. It calls for adaptive legal frameworks, greater interdisciplinary collaboration, and proactive policymaking. These findings underscore the imperative to build ethical and accountable AI governance models that safeguard individual rights without stifling innovation.
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