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Paper short abstract

By treating technical architecture itself as the primary site of critique, this paper challenges the "technological neutrality" assumption in autonomous driving, demonstrating how capability boundaries structurally encode inequality across perception, decision-making, and systems layers.

Paper long abstract

Critiques of AI often target deployment practices, dataset biases, or governance failures, leaving the technical architecture itself unquestioned. This paper argues that such approaches risk reproducing "uncontroversial accounts of AI" (Suchman, 2023), by accepting the architecture as a given and debating only its uses. We propose instead to treat technical architecture as the primary site of critical inquiry, following Winner's insight that artifacts have politics.

Taking autonomous driving as a case study and combining close reading of engineering literature with STS-informed analysis, we examine how the "technological neutrality" assumption fails at three architectural layers. At the perception layer, dynamic range ceilings and feature extraction bottlenecks produce irreducible detection disparities disproportionately affecting darker-skinned pedestrians, revealing that capability boundaries are simultaneously equity boundaries. At the decision-making layer, end-to-end neural architectures dissolve accountability chains, whereby explainability techniques generate post-hoc rationalizations rather than causal transparency, institutionalizing historical bias while rendering correction structurally impossible. At the systems layer, the shift from HD maps to world models replaces visible geographic exclusion with covert temporal exclusion, redistributing risk along socioeconomic fault lines the dominant SAE taxonomy renders invisible.

This paper operates from within the technical rather than imposing ethical frameworks from outside. By engaging directly with engineering literature and system design logic, we show that architectural non-neutrality is an empirical finding, not a philosophical conjecture. Current autonomous driving systems have never incorporated equity as a foundational design constraint, and meaningful critique must engage with architecture as such rather than limiting itself to configuration or deployment.