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

URL SCAN: Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
FIRST LINE: # Computer Science > Computers and Society

The Dissection

This paper builds an administrative instrument for making transport GenAI deployment measurable and defensible. It combines persona-based LLM disparity tests, synthetic-crash-data validation, public-attitude modeling, and a composite Sociotechnical Risk Index.

Its strongest contribution is diagnostic: congestion-pricing advice shows high persona dispersion; CART synthetic crash records fail every conditional validity test; and approval categories are unstable, flipping 75% under weight perturbation. The paper exposes that crude governance labels are structurally unreliable.

But the audit remains inside the deployment frame. It asks how to manage unequal outputs, defective data, and public trust—not who owns the systems, who captures the productivity gains, or who loses economically necessary work when transport intelligence is automated.

The Core Fallacy

The central error is treating a power-and-ownership problem as an observability-and-indexing problem.

A more precise risk score cannot solve the Discontinuity Thesis. It can identify which populations receive worse advice or which synthetic records distort reality. It cannot prevent AI from achieving durable superiority across cognitive transport work, institutions from failing to preserve human-only roles at scale, or productive participation from collapsing.

The audit mistakes detectability for controllability. Once risk is measured, the institution can claim governance. The underlying displacement continues.

Hidden Assumptions

• Transport agencies can act on audit findings faster than deployment incentives force adoption.
• Distributional risk can be reduced to a continuous index without converting political choices into technical weights.
• Demographic cues in prompts adequately represent real transport disadvantage.
• LLM judges and Wasserstein-based dispersion measures capture consequential harm rather than merely measurable output variation.
• Synthetic-data validity implies usefulness for safety or policy decisions.
• Public trust is a meaningful governance endpoint rather than a variable to be managed while control concentrates.
• Better approval procedures can preserve legitimate human participation in an AI-dominated system.
• Institutions deploying the models have both the authority and incentive to reject systems that increase their own efficiency.

The 75% classification flip rate is not just a methodological warning. It shows that governance outcomes depend heavily on discretionary weighting. The supposedly objective layer is carrying political judgment under a statistical disguise.

Social Function

Primary classification: transition management.

Secondary classifications: partial truth, prestige signaling, and ideological anesthetic.

This is not empty copium. The paper identifies genuine failures and supplies useful tests. Its institutional function, however, is to make an accelerating transition appear governable through audits, indices, and sensitivity reports. It gives agencies a vocabulary for managing visible harms without confronting the ownership structure that determines who benefits and who becomes economically redundant.

The likely result is procedural legitimacy: deployment proceeds, disparities are documented, reports are issued, and the displaced receive measurement instead of power.

The Verdict

A technically serious audit of the surface injuries, not an analysis of the underlying kill mechanism. It can improve detection of discriminatory advice and invalid synthetic data. It cannot halt the structural sequence: cognitive automation, institutional inability to preserve human-only domains, and collapse of productive participation.

Under the Discontinuity Thesis, this paper is transition-management equipment. Useful as a sensor. Powerless as a brake.