Onlife Harms: Uber and Sexual Violence
Bibliographic record
Abstract
Uber markets itself as a technology company that is managed primarily by ML algorithms with the support of human engineers. Yet, in its 2019 Report, the role that its technology played in relation to sexual violence is, for all intents and purposes, absent. Likewise, solutions dealing specifically with the role of technology in facilitating gender-based violence are also missing from the series of initiatives in which Uber has invested that are aimed at preventing sexual violence. Uber was not sufficiently rigorous in defining the problem it was trying to solve. It was a missed opportunity that has resulted in continued harm.\nThere is equally a dearth of analysis in respect of how technology is used as a tool by perpetrators to broaden the scope of sexual violence in the case law involving Uber. This may suggest that the courts do not have the tools to deal with the role that technology plays. Evidence of this can be found not only in cases of sexual violence but elsewhere. Uber Technologies Inc. v. Heller is a good example. While the Supreme Court of Canada’s decision here ‘‘brought the doctrine of unconscionability from the backburners to the forefront of contract law,” the Court failed to recognize the fundamental role that technology played in the case. Effectively, the Court left the role of technology in Uber, on the backburners of contract law, when in fact it should have been at the forefront.\nThis leads to the key question: what path should Uber take to deal with TFGBV? Uber must recognize that to have an algorithm that is not toxic, it must deal with its toxic environment.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".