Hiring Algorithms in the Canadian Private Sector: Examining the Promise of\tGreater Workplace Equality
Bibliographic record
Abstract
Private-sector employers are increasingly using hiring algorithms as a tool for screening job applicants, comparing qualifications, and ultimately determining which candidates should be selected. Within this context, hiring algorithms make no small promise: a hiring process that is not only more efficient and effective, but also more supportive of workplace equality. This promise rests largely on the notion that traditional human-driven models of hiring are beset by subjective biases and prejudices, whereas hiring algorithms, which are driven by hard data and objective evidence, can eliminate certain human biases and prejudices, thereby promoting workplace equality. But can hiring algorithms deliver on this promise? This article, which focuses on issue identification, argues that while hiring algorithms may, when used carefully, assist in mitigating certain hiring discrimination risks, their capacity to do so is not without limits, and they may in fact introduce certain concerns over systemic discrimination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".