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

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.

Full Characterization of Adaptively Strong Majority Voting in Crowdsourcing

A commonly used technique for quality control in crowdsourcing is to task the workers with examining an item and voting on whether the item is labeled correctly. To counteract possible noise in worker responses, one solution is to keep soliciting votes from more workers until the difference between the numbers of votes for the two possible outcomes exceeds a pre-specified threshold {\delta}. We show a way to model such {\delta}-margin voting consensus aggregation process using absorbing Markov chains. We provide closed-form equations for the key properties of this voting process -- namely, for the quality of the results, the expected number of votes to completion, the variance of the required number of votes, and other moments of the distribution. Using these results, we show further that one can adapt the value of the threshold {\delta} to achieve quality-equivalence across voting processes that employ workers of different accuracy levels. We then use this result to provide efficiency-equalizing payment rates for groups of workers characterized by different levels of response accuracy. Finally, we perform a set of simulated experiments using both fully synthetic data as well as real-life crowdsourced votes. We show that our theoretical model characterizes the outcomes of the consensus aggregation process well.