Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?
This addresses fairness issues in admissions for institutions, but it is incremental as it builds on existing fairness methods with a hybrid approach.
The study tackled fairness-accuracy trade-offs in machine learning models using a university admissions dataset with 870 applicants, finding that ML models (XGB, Bi-LSTM, KNN) outperformed human evaluators in fairness consistency by 14.08% to 18.79%.
Fairness in both Machine Learning (ML) predictions and human decision-making is essential, yet both are susceptible to different forms of bias, such as algorithmic and data-driven in ML, and cognitive or subjective in humans. In this study, we examine fairness using a real-world university admissions dataset comprising 870 applicant profiles, leveraging three ML models: XGB, Bi-LSTM, and KNN, alongside BERT embeddings for textual features. To evaluate individual fairness, we introduce a consistency metric that quantifies agreement in decisions among ML models and human experts with diverse backgrounds. Our analysis reveals that ML models surpass human evaluators in fairness consistency by margins ranging from 14.08\% to 18.79\%. Our findings highlight the potential of using ML to enhance fairness in admissions while maintaining high accuracy, advocating a hybrid approach combining human judgement and ML models.