Structural Discrimination and Autonomous Vehicles: Immunity Devices, Trump Cards and Crash Optimisation
Bibliographic record
Abstract
This paper examines the potential for structural discrimination to be woven into the fabric of autonomous vehicle developments, which remain underexplored and undiscussed. The prospect for structural discrimination arises as a result of the coordinated modes of autonomous vehicle behaviour that is prescribed by its code. This leads to the potential for individuated outcomes to be networked and thereby multiplied consistently to any number of vehicles implementing such a code. 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. The spectre of implicit structural discrimination is therefore raised by the orderly and stable rearrangement of biases that may be expressed by the controlling algorithm. The potential for a much more pernicious form of active structural discrimination looms with the possibility of crash optimisation impulses in which a protective shield is cast over those individuals in which society may have a vested interest in prioritising or safeguarding. A stark dystopian scenario is introduced to sketch the contours whereby personal beacons signal individual identity, and potentially relative worth, to autonomous vehicles engaging in a crash damage calculus. At the risk of introducing these ideas into the development of autonomous vehicles, this paper hopes to spark a debate to foreclose these eventualities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".