Explanation and Validation as Conditions for Trust in Judicial AI in Bangladesh

Abstract Judicial artificial intelligence requires grounds for warranted reliance rather than confidence alone. This study examines how respondents in Bangladesh distinguish understandable reasons, independent validation and opportunities to challenge AI-supported recommendations. A cross-sectional analysis of 385 adult, AI-aware respondents uses seven questionnaire items without treating their mixed normative and conditional wording as a validated explainability-trust scale. The analysis combines paired endorsement comparisons, proportional-odds models with robust covariance, multiplicity adjustment and paired-outcome coefficient comparisons. Independent-validation-contingent trust was endorsed by 247 respondents (64.2%), compared with 215 (55.8%) for explanation-contingent trust: a paired difference of 8.3 percentage points (95% bootstrap interval 1.6–15.1; Holm-adjusted p = .021). Access to review or appeal received greater endorsement than the ability to question recommendations (85.2% versus 65.2%; difference 20.0 points, 95% interval 14.3–25.7). None of the eight focal associations between four governance expectations and the two trust propositions remained distinguishable from zero after Holm adjustment. The joint test of differences between the four outcome-specific coefficients was also inconclusive (p = .236). These findings identify a descriptive separation between explanations, validation and remedies while providing limited adjusted evidence about how those expectations align within respondents. The predominantly student sample does not establish judicial practice, actual reliance or causal effects of explanations. The article develops a layered account of judicial AI governance that connects understandable reasons with independent evaluation and effective review, and specifies a measurement and experimental agenda for testing calibrated reliance.