Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust

• URL: http://arxiv.org/abs/2609.11611v1
• Date: Thu, 10 Sep 2026 14:29:09 GMT
• Title: Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
• Abstract summary: Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support.<n>We develop a Distributional Audit that integrates algorithmic equity, synthetic-data validity, and public-attitude into one empirical pipeline.
• Score: 1.089614199781423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support. Existing governance frameworks lack transport-specific statistical tools to measure distributional risks across heterogeneous populations. We develop a Distributional Sociotechnical Audit (DSA) that integrates algorithmic equity, synthetic-data validity, and public-attitude heterogeneity into one empirical pipeline. The audit analyzes 5,760 persona-controlled queries to four LLM families across 12 demographic cues and four transport topics, uses two cross-family judges and a Wasserstein-2 Equity Dispersion Index, tests three FARS crash-record generators with conditional projected maximum mean discrepancy (cpMMD), fits a Bayesian ordered-logit model to Pew American Trends Panel Wave 152 (N = 4,538), and combines the signals into a continuous Sociotechnical Risk Index. Congestion-pricing advice has the highest persona-based dispersion (mean EDI = 1.96; highest direct EDI = 2.20). CART synthetic crash records fail all conditional tests (p < 0.001), while the Gaussian copula has borderline conditional stress (p = 0.105) despite passing marginal checks. Attitudes to AI vary across demographic strata. Distributional audits and continuous risk indices with sensitivity reporting offer a more defensible basis for transport GenAI governance than categorical approval tiers, which show a 75% assignment flip rate under weight perturbation.

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