A Tale of Two: Can Gender Equality and Efficiency Go Hand in Hand?

This paper examines the tension between efficiency and gender equality in hiring. Companies aim to streamline processes while ensuring fairness, particularly in achieving gender balance. Despite advancements in hiring algorithms, studies suggest lingering biases against women. Using real-world recruitment data, I analyze the gender equity implications of two popular hiring tools: predictive algorithms and assessment software in full automation. I replicate two widely used resume screening mechanisms—one predictive, the other assessment-based—and contrast them with human recruiters. The findings reveal a significant discrepancy: predictive algorithms maintain gender inequality and mimic human recruiters’ selections, while assessment software significantly boosts female candidates' chances by 79 percentage points (with a baseline selection rate of 22 percentage points for female applicants) and enhances the overall qualifications of the selected pool. These results challenge existing research on AI-driven hiring, suggesting a cautiously optimistic approach to exploring hiring algorithms’ role in fostering gender equality.