Examining Potential Gender Bias in Automated Recruitment Systems in Canada and Its Impact on Immigrant Women
Bibliographic record
Abstract
<p>This major research paper examines the potential gender bias in automated recruitment systems, also known as applicant tracking systems (ATS), and how it impacts immigrant women in Canada. It explores the relationship, interactions, and intersections between gender and artificial intelligence from an anthropological perspective to assess the impact of this technological trend on the process of acquiring the right job roles based purely on skills, particularly for immigrant women. Additionally, it studies the social, economic, and diachronic hardships that immigrant women may experience that artificial intelligence systems may ignore when considering skills data sets. Using studies on gender, artificial intelligence, technological fetishism, data feminism, digital discrimination, and AI recruitment, this paper focuses on questions such as whether AI is biased or if the people creating it are, who produces AI, and whether AI has agency. The research addresses these questions to help reimagine AI to make it useful for all groups without discrimination. For this research, the author, an immigrant woman in Canada, relied on autoethnography to collect and analyze qualitative data of her job search on LinkedIn. The insights drawn from this have been presented in the paper.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".