当生成式人工智能进入交通领域时，谁承担风险？一项关于算法公平性、合成数据有效性和公众信任的分布性社会技术审计
Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
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中文总结 AI 辅助
本研究提出分布性社会技术审计（DSA），整合算法公平性、合成数据有效性与公众态度，通过实证分析揭示生成式AI进入交通领域时的分布性风险，并主张采用连续风险指数替代分类治理。
中文摘要 AI 辅助
生成式人工智能正通过面向出行者的咨询建议、合成碰撞记录生成和政策决策支持进入交通领域。现有的治理框架缺乏针对交通领域的统计工具来衡量不同人群之间的分布性风险。我们开发了一种分布性社会技术审计（DSA），将算法公平性、合成数据有效性和公众态度异质性整合到一个实证流程中。该审计分析了针对四个LLM家族的5,760个基于人物角色的查询，涵盖12个人口统计线索和四个交通主题，使用两个跨家族评判器和一个Wasserstein-2公平离散指数，测试了三个FARS碰撞记录生成器并采用条件投影最大平均差异（cpMMD），将贝叶斯有序逻辑模型拟合到皮尤美国趋势小组第152波数据（N = 4,538），并将这些信号合并为一个连续的社技术风险指数。拥堵定价建议具有最高的人物角色离散度（平均EDI = 1.96；最高直接EDI = 2.20）。CART合成碰撞记录未能通过所有条件测试（p < 0.001），而高斯copula尽管通过了边际检验，但存在临界条件压力（p = 0.105）。对人工智能的态度在不同人口统计阶层中有所差异。分布性审计和带有敏感性报告的连续风险指数为交通生成式人工智能治理提供了比分类批准层级更可辩护的基础，后者在权重扰动下显示出75%的分配翻转率。
英文摘要
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.
发表机构
- Texas State University（德克萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。
