Judicial Readiness for Artificial Intelligence Adoption in Bangladesh: An Analysis of Functional, Normative, and Ethical Drivers
DOI:
https://doi.org/10.5281/zenodo.24012211Abstract
Globally, Artificial Intelligence (AI) is being used consistently to improve procedural transparency, uniformity, and efficiency in conventional judicial processes governed by the substantive and procedural laws of countries. With an emphasis on six areas—evidence analysis, sentencing, case management, transparency and accountability, investigation and interagency coordination, and ethical safeguards—this study empirically investigates the judicial preparedness for AI adoption in the judicial process in Bangladesh. The study examines how views of judges regarding AI use are influenced by functional benefits and ethical readiness using Partial Least Squares Structural Equation Modeling (PLS-SEM) and a quantitative, cross-sectional survey of 100 judicial officers from various court tiers. The results show that while AI-assisted sentencing and investigative cooperation have non-significant effects on court readiness, views of AI-assisted case management, ethical protections, transparency, and rule-of-law compliance have a substantial impact. The significance of ethical governance, human oversight, and institutional trust is further highlighted by qualitative interviews, which show that judges largely see AI as a decision-support tool rather than a replacement for judicial discretion. By incorporating socio-legal and technological governance viewpoints into a model of judicial AI adoption, this paper makes a theoretical contribution. From a practical standpoint, the findings help judicial administrators and lawmakers create AI interventions that improve judicial efficiency, preserve procedural integrity, and protect public trust. The small sample size, Bangladeshi emphasis, and absence of other stakeholders like attorneys and court employees are among the limitations, which point to areas for further study on the deployment of AI in judicial and institutional settings.
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