Exploration-based algorithms can improve hiring quality and diversity


By Brian Eastwood | MIT Ideas Made to Matter | September 30, 2020

MIT researchers have developed a new approach for using algorithms in the recruiting process that can help companies draw talent from a more diverse pool of job applicants. The approach yields more than three times as many Black and Hispanic candidates than companies may have considered using traditional resume screening algorithms. The algorithm also generates a set of interviewees that is more likely to receive and accept a job offer, which can help companies streamline the hiring process. A new working paper, “Hiring as Exploration,” details the results.

Firms are increasingly turning to algorithms to help them make hiring decisions. Algorithms hold the promise of saving firms time — they can process thousands of applications much faster than a human recruiter could — and also potentially improving screening decisions by unearthing predictors of applicant performance that humans might miss.

Traditional hiring algorithms look for characteristics of a job applicant that predict future success, based on a historical training dataset of applicants who have been interviewed or hired in the past. This type of approach, known as supervised learning, works well when firms have a lot of data on past applicants, and when the qualities that predict past success continue to predict future success. Yet there are many instances when both these assumptions may not be true. For example, applicants from non-traditional backgrounds may be under-represented in the training dataset, making it more difficult for firms to accurately predict their performance. Moreover, skill demands may change over time: firms hiring workers in 2020 may place more emphasis on an employee’s ability to work effectively in a remote setting.

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