Inferior: The Challenges of Gender Parity in the Artificial Intelligence Ecosystem-A Case for Canada
Bibliographic record
Abstract
Artificial Intelligence (AI) systems are gaining momentum in complementing and/or replacing performing tasks typically done with the aid of human ability. AI systems, inherently human creations, are, however, beset by, wittingly or unwittingly, so-called male chauvinism, despite all the advancements made in the progress of civilization to make inroads for women's equitable participation in the labor force, particularly in relation to the digital economy, and more importantly, AI. In regards to the Canadian context, this perspective has examined the evidence to find research highlighting gender representation in the Canadian AI ecosystem. We found a lack of studies on women and their contribution to AI-related activities. Canadian women's participation in their country's AI sector therefore should go beyond mere instruments such as the Montreal Declaration for a Responsible Development of AI, and disjointed interests. On a more general level, the paucity in a paradigm shift toward AI-female friendly policies from design phase to implementation omits the female voice for adequate representation for action. Advocating for Canadian women in the AI sector requires a voice of unison best achieved through parliamentary action. This perspective is thus issuing a clarion call to attaining gender fairness and equity, global principles under the United Nations (UN) Sustainable Development Goals, to which the Government of Canada is committed.
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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 | 0.001 |
| 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) | 0.000 | 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".