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<p>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.</p>

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Gender effects and racial biases in mate selection as revealed by machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Aimee Hastings-James,
  • AJ Hinman,
  • Derek Berger,
  • Jacob Levman

摘要

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.