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. In an extension, we show that exploration-based algorithms are also able to learn
more effectively about simulated changes in applicant hiring potential over time. Together, our
results highlight the importance of incorporating exploration in developing decision-making
algorithms that are potentially both more efficient and equitable. |
