Algorithmic transparency in legal systems represents a critical challenge as artificial intelligence increasingly influences judicial decision-making, risk assessment, and administrative processes. This research examines the regulatory landscape governing AI explainability through comprehensive analysis of the EU AI Act, GDPR, and emerging national frameworks. The study identifies fundamental tensions between algorithmic opacity and due process requirements, evaluating how legal systems balance technological innovation with constitutional rights. Through comparative analysis of regulatory approaches and landmark judicial decisions, this research demonstrates that current transparency mechanisms remain inadequate for complex machine learning systems deployed in legal contexts. The findings reveal that meaningful algorithmic accountability requires multi-layered frameworks combining technical explanations, legal interpretability standards, and institutional oversight mechanisms. This research proposes conceptual solutions for enhancing AI transparency through standardized documentation requirements, interpretability benchmarks, and participatory governance structures applicable to legal systems worldwide, including emerging jurisdictions such as Uzbekistan.

algorithmic transparency, legal systems, AI explainability, due process, regulatory frameworks, judicial decision-making

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