Three Types of Structural Discrimination Introduced by Autonomous Vehicles
Bibliographic record
Abstract
The advent of autonomous vehicles has been hailed by commentators as introducing an improvement for traffic safety by promising to reduce the overall number of road accidents as the technology matures. Some advocates even hint at a moral imperative to structure incentives and smooth over barriers in order to induce widespread usage of autonomous vehicle in order to actualize this potential. The orientation towards safety concerns, however, foregrounds the debate on crash-optimization and imports the trolley-problem thought-experiments to the question of autonomous vehicles. This paper examines the potential for three types of structural discrimination to be woven into the fabric of these developments. First, given the emphasis placed upon decisional agency by trolley-problem scenarios, there will be systematic privileging of the occupant vis-à-vis pedestrians and other third-parties. Second, there is the prospect for structural discrimination arising from the coordinated modes of autonomous vehicle behavior that is prescribed by its code, or which is converged upon through learning algorithms operating towards similar goals and within similar constraints. This leads to the potential for hitherto individuated outcomes to be networked and thereby multiplied across fleets of vehicles. The aggregated effects of such algorithmic policy preferences will thus cumulate in the reallocation of benefits and burdens to certain categories of persons in a relatively stable manner. This in turn raises the spectre of a more pernicious form of active structural discrimination where the possibility of crash-optimization casts a protective shield over certain individuals at the cost of third-parties. Third, the introduction of autonomous vehicles within the framework of crash-optimization will likely precipitate infrastructural changes, which in a literal sense, will introduce or exacerbate structural forms of discrimination with regard to human access to public space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".