Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes

TL;DR

This paper investigates how algorithmic hiring tools impact diversity, revealing that reducing human-algorithm similarity in screening can improve final hire diversity, especially when tailored to identify overlooked candidates.

Contribution

It introduces a new algorithmic approach that diversifies shortlists by selecting candidates overlooked by managers, improving diversity without sacrificing quality.

Findings

Enforcing equal shortlists has limited impact when screening criteria align closely with managers.

Higher correlation between algorithm and human evaluation reduces final hire diversity.

The proposed method significantly increases gender diversity in final hires.

Abstract

Algorithmic tools are increasingly used in hiring to improve fairness and diversity, often by enforcing constraints such as gender-balanced candidate shortlists. However, we show theoretically and empirically that enforcing equal representation at the shortlist stage does not necessarily translate into more diverse final hires, even when there is no gender bias in the hiring stage. We identify a crucial factor influencing this outcome: the correlation between the algorithm's screening criteria and the human hiring manager's evaluation criteria -- higher correlation leads to lower diversity in final hires. Using a large-scale empirical analysis of nearly 800,000 job applications across multiple technology firms, we find that enforcing equal shortlists yields limited improvements in hire diversity when the algorithmic screening closely mirrors the hiring manager's preferences. We propose a complementary algorithmic approach designed explicitly to diversify shortlists by selecting candidates likely to be overlooked by managers, yet still competitive according to their evaluation criteria. Empirical simulations show that this approach significantly enhances gender diversity in final hires without substantially compromising hire quality. These findings highlight the importance of algorithmic design choices in achieving organizational diversity goals and provide actionable guidance for practitioners implementing fairness-oriented hiring algorithms.