Single-vendor screening risks blocking candidates across multiple employers
AI is changing how employers hire workers.
Today we are publishing our research over the past four years into this high-stakes application of AI.
We independently studied the impacts of deployed AI hiring tools based on the real outcomes for 3.3 million people.
New research reveals a troubling trend: AI hiring tools show racial bias and systematically reject candidates. What happens then when most employers use the same AI screening algorithms? Read more about the first large-scale study of hiring AI: https://hai.stanford.edu/news/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection
AI is changing how employers hire workers.
Today we are publishing our research over the past four years into this high-stakes application of AI.
We independently studied the impacts of deployed AI hiring tools based on the real outcomes for 3.3 million people.
To reveal this adverse impact, we need to consider each position separately.
While our data comes from a single vendor, the vendor mediates decisions for 1700 positions at 150 firms.
Notably, just 23% of the positions yield 80% of the adverse impact for Blacks (Matthew effect)
We find that 18% Asian and 30% Black applicants are adversely impacted based on the relevant US federal standard.
If these groups were selected at the same rate as the most-selected group (generally White), then 40k additional applications would have been recommended.
Single-vendor screening risks blocking candidates across multiple employers
AI is changing how employers hire workers.
Today we are publishing our research over the past four years into this high-stakes application of AI.
We independently studied the impacts of deployed AI hiring tools based on the real outcomes for 3.3 million people.
