Gender effects and racial biases in mate selection as revealed by machine learning
Bibliographic record
Abstract
This study investigates the potential role of machine learning (ML) technology for predicting a match, or mutual interest, in the context of speed dating. Modern machine learning technologies (light gradient boosting machine - lgbm, random forest, logistic regression, stochastic gradient descent, k nearest neighbour), exhaustively combined with feature selection methods (filter-based association, filter-based prediction, embedded lgbm, embedded linear, redundancy aware step up wrapper), were applied to a speed dating dataset, and tasked with predicting a match (mutual interest from speed dating participants). Our analysis employed public-domain ML software combined with a public-domain dataset, supporting reproducibility of study findings. Results indicate that ML models can predict a match with 85.4 to 86.4% accuracy. The creation of ethical ML applications in this domain, including those blinded to issues of race, and specific to each gender, are explored as part of this analysis. Results also demonstrate that it is possible to create race-blinded ML models with approximately equal performance to those biased by racial information, thus supporting the creation of more ethical, inclusive, and behavior-focused technologies.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".