Simple rules outperform machine learning for personnel selection: insights from the 3rd annual SIOP machine learning competition

Abstract

AbstractMachine learning (ML) algorithms are often assumed to be the most accurate way of producing predictive models despite problems with explainability and adverse impact. The 3rd annual Society for Industrial and Organizational Psychology Machine Learning Competition sought to find ML models for personnel selection that could balance the best of ML prediction with the constraint of minimizing selection bias based on race and gender. To test the possible advantages of simple rules over ML algorithms, we entered a simple and explainable rule-based model inspired by recent advances in model comparison. This simple model outperformed most ML models entered and was comparable to the top performers while retaining positive qualities such as explainability and transparency.