AI and Technology-Facilitated Violence and Abuse
Bibliographic record
Abstract
Artificial intelligence (AI) is being used—and is in some cases specifically designed—to cause harms against members of equality-seeking communities. These harms, which we term “equality harms” have individual and collective effects, and emanate from both “direct” and “structural” violence. Discussions about the role of AI in technology-facilitated violence and abuse (TFVA) sometimes do not include equality harms specifically. When they do, they frequently focus on individual equality harms caused by “direct” violence (e.g. the use of deepfakes to create non-consensual pornography to harass or degrade individual women). Often little attention is paid to the collective equality harms that flow from structural violence, including those that arise from corporate actions motivated by the drive to profit from data flows (e.g. algorithmic profiling). Addressing TFVA in a comprehensive way means considering equality harms arising from both individual and corporate behaviours. This will require going beyond criminal law reforms to punish “bad” individual actors, since responses focused on individual wrongdoers fail to address the social impact of the structural violence that flows from some commercial uses of AI. Although, in many cases, the harms occasioned by these (ab)uses of AI are the very sort of harms that law is used to address or has been used to address, existing Canadian law is not currently well placed to meaningfully address equality harms.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.063 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".