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

arXiv:2609.11611v1 Announce Type: new 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.

The item discusses risks of GenAI in transport, but also presents a potential solution.

The study highlights significant distributional risks and failures in synthetic data validity for generative AI in transport, indicating inequitable impacts and public trust issues. While no extreme dystopian outcome is claimed, these pose material concerns for fairness and governance in AI deployment in transportation.

This is a methodological audit revealing real measured weaknesses (demographic dispersion in advice, failed synthetic-data validity tests, unstable governance tiers) but does not itself describe deployed harm at scale, only latent risk in an emerging application domain. The findings are moderately concerning for equity and governance reliability rather than catastrophic.

The study identifies significant risks in the use of generative AI in transport. High distributional risk and failure in synthetic crash record tests indicate potential harm. Variability in public attitudes suggests challenges in widespread acceptance.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   c4028c86-9050-4b4a-8f44-d826016f3827
           https://arxiv.org/abs/2609.11611
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EVIDENCE   3 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -54  (Adverse)
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CONFIDENCE 76%