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

New research introduces a Distributional Sociotechnical Audit (DSA) to assess the risks of generative AI in transport, addressing gaps in current governance frameworks. The audit integrates algorithmic equity, synthetic-data validity, and public attitudes, applying persona-controlled queries to large language models and testing crash-record generators to measure distributional risks across diverse populations.

The introduction of generative AI into critical sectors like transport necessitates robust frameworks for risk assessment, particularly concerning equitable outcomes and data integrity. This research provides a structured approach to identify and mitigate biases and validity issues, which is crucial for maintaining public trust and ensuring the responsible deployment of AI technologies.

Generative AI is being deployed in transportation for traveler advisories, synthetic crash-record generation, and policy decision support.

Forward consideration, not a verified fact.