We also study new concerns brought about by the shared dependence on a single hiring vendor. In our prior work, we theorized that algorithmic monocultures in which many employers came to rely on the same algorithmic recommendations could lead to some people being shut out from jobs. Using our large dataset of real hiring AI recommendations, we test our hypothesis. We find that people who submit multiple applications to positions screened by the same algorithmic hiring vendor are more likely to be rejected from every position to which they apply than would be true if the companies made decisions statistically independently from one another. Ten percent of applicants who submit four applications are rejected from all the places to which they apply.

Our research also found that this pattern does not appear to be the case in other circumstances. We analyzed data from the largest prior study of hiring decisions, which sent 83,000 applications to 108 Fortune 500 firms during the same time period as our study and did not focus on whether AI was used to make decisions. We found that the rate at which applicants were rejected from every firm they applied to in this data was no higher than what you’d expect if each company decided independently of the others. 

This suggests market concentration matters: As a single hiring vendor comes to dominate screening for an industry, it may be more likely that candidates are shut out.