Algorithmic Bias Concerns and Acceptance of AI Supported Legal Decision Making in Bangladesh

Artificial intelligence can assist legal work while creating risks of discrimination, opaque reasoning and diminished accountability. This study examines whether concern about biased training data coexists with perceived usefulness and support for safeguards among respondents to a Bangladesh legal-AI survey. The analysis uses 385 adult, AI-aware survey records supplied by the research team. Because the questionnaire combines risk perceptions, utility and governance preferences, the analysis retains individual items rather than presenting the blocks as validated scales. Descriptive distributions, paired endorsement comparisons and exploratory Spearman correlations assess distinct aspects of the research question. Concern about biased training data was endorsed by 334 respondents (86.8%), regular audits by 317 (82.3%) and a right to review or appeal by 328 (85.2%). AI assistance with organising information was endorsed by 247 (64.2%); 219 (56.9%) endorsed both this usefulness item and bias concern. The rank correlation between bias concern and perceived usefulness was small (ρ = .075), with no clear evidence of association after adjustment for multiple comparisons. These self-reported attitudes cannot establish the fairness of a system, causal effects or the actual conditions under which a respondent would rely on an AI recommendation. The results point to the importance of measuring perceived benefits, risk and procedural protections separately. The article integrates technology acceptance and procedural justice as a framework for future scenario-based studies and proposes proportionate auditing, meaningful human review and accessible contestation for legal decision support.