APPLIED AI JUSTICE DELIVERY AND AUTONOMOUS LEGAL DECISIONMAKING FRAMEWORK

The increasing integration of artificial intelligence into legal and justice systems has generated renewed interest
in data driven decision support, procedural efficiency, and improved access to justice, particularly within
developing and transitional legal environments. This study examines the emerging landscape of applied AI justice
delivery and autonomous legal decision-making frameworks, with a focus on governance, confidentiality,
transparency, and human oversight. Using a qualitative doctrinal and policy analysis supported by comparative
evidence from existing judicial digitization initiatives, the paper evaluates how intelligent systems can support
courts without undermining due process, accountability, or public trust. Findings indicate that jurisdictions
adopting structured digital workflows and AI assisted legal analytics report average reductions in case processing
time ranging between 30% and 45%, while administrative backlog reduction reaches approximately 40% in earlystage implementations. The surveyed judicial administrators and legal professionals show that 65% associate AI
assisted tools with improved procedural efficiency, 58% report enhanced consistency in case management, and
52% identify improved access to justice through digital platforms. However, only 44% express confidence in
current data protection safeguards, highlighting persistent risks related to confidentiality, cybersecurity, and
algorithmic opacity. The paper contributes to ongoing academic and policy debates by articulating a governance
grounded framework for AI assisted justice delivery that prioritizes confidentiality, transparency, and ethical
accountability. The findings provide practical insights for courts, policymakers, and legal institutions seeking to
modernize justice systems while safeguarding fundamental legal principles.