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

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. Keywords: explainable artificial intelligence; conditional trust; judicial decision support; procedural justice; independent validation; Bangladesh; SDG 16

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Publication Details

Journal
Journal of State Government and Mass Media
Published
2026-10-30
DOI
https://doi.org/10.64907/xkmf.v04i04.jsgmm.1
Primary Topic
Artificial Intelligence in Law
Type
article
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article

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

Kazi Abdul Mannan, Israt Jahan Maisha, Maksuda Ferdous, Israt Jahan Maisha
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Journal of State Government and Mass Media
Artificial Intelligence in Law
article

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

Kazi Abdul Mannan, Israt Jahan Maisha, Maksuda Ferdous, Israt Jahan Maisha
article en

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. Keywords: explainable artificial intelligence; conditional trust; judicial decision support; procedural justice; independent validation; Bangladesh; SDG 16

Journal of State Government and Mass MediaVol. 4(4)
Shanto-Mariam University of Creative Technology (BD)
Peace, justice, and strong institutions
Openalex Percentile: Top 4%
Artificial Intelligence in Law
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