Hiring as Exploration

TL;DR

This paper models hiring as a contextual bandit problem, introducing an exploration-focused algorithm that improves candidate quality and diversity compared to traditional supervised learning methods.

Contribution

It develops a novel resume screening algorithm that incorporates exploration to enhance both hiring outcomes and demographic diversity.

Findings

Improves candidate hiring rates

Increases demographic diversity

Outperforms traditional supervised learning algorithms

Abstract

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.