Recognizing Operators’ Duties to Properly Select and Supervise AI Agents – A (Better?) Tool for Algorithmic Accountability
Bibliographic record
Abstract
In November of 2020, the Privacy Commissioner of Canada proposed creating GDPR-inspired rights for decision subjects and allowing financial penalties for violations of those rights. Shortly afterward, the proposal to create a right to an explanation for algorithmic decisions was incorporated into Bill C-11, the Digital Charter Implementation Act. This commentary proposes that creating duties for operators to properly select and supervise artificial agents would be a complementary, and potentially more effective, accountability mechanism than creating a right to an explanation. These duties would be a natural extension of employers’ duties to properly select and retain human employees. Allowing victims to recover under theories of negligent hiring or supervision of AI-system-as-agents would reflect their increasing (but less than full) autonomy and avoid some of the challenges that victims face in proving the foreseeability elements of other liability theories.
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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 | 0.036 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".