Privacy Concern and Conditional Willingness to Adopt AI Legal Platforms in Bangladesh

Privacy concerns may discourage the use of artificial intelligence in legal services while also motivating interest in platforms with credible safeguards. These responses are often collapsed into a single adoption construct, obscuring whether respondents reject the technology or condition its use on protection. This study examines that distinction in 385 adult, AI-aware survey records from Bangladesh, including 242 law students. Five statements separately measure disclosure concern, self-attributed inhibition, conditional willingness under strong privacy controls, preference for meaningful data control, and the perceived influence of trust. Analyses combine joint response patterns, paired endorsement contrasts, ordinal correlations and exploratory logistic models with robust covariance estimates. Disclosure concern was endorsed by 56.1%, privacy-related inhibition by 75.6%, conditional willingness under strong controls by 72.7%, meaningful-control preference by 53.2%, and the influence of trust by 79.0%. Inhibition and conditional willingness coexisted in 216 records (56.1%). Concern had a small positive ordinal association with conditional willingness (ρ = 0.149; Holm-adjusted p = 0.013), but its adjusted association with binary endorsement was uncertain (odds ratio 1.29; 95% interval 0.98-1.69). None of the four focal adjusted model tests met the 0.05 threshold after Holm correction. The findings identify conditional acceptance within the recorded sample, rather than an observed privacy paradox or an effect on actual adoption. The article contributes a distinction between concern, attributed inhibition and safeguard-contingent willingness. It recommends evaluating concrete privacy controls through tasks that measure informed choices, alongside technical protection. Undocumented recruitment and fieldwork dates constrain population inference and temporal interpretation.