AI hiring tools show racial bias | Stanford Report. Really!

It is very disturbing when even an elite university like Stanford engages in this racial bias propaganda and demagoguery! It even features this propaganda in their Stanford Report!

Notice the study is a mixtum compositum! What does e.g. "systemic rejection" have to do with "racial bias"?

When elite university professors hear the grass grow for funding opportunities etc.!

It is regrettable and disappointing when even highly cited, very well known ML & AI researchers like Dan Jurafsky and Percy Liang are involved in this.

It is surprising if not mysterious why elite researchers "don’t yet know why these tools are biased". Are the ML & AI models not trained on tons of human (biased) data?

Furthermore, it appears that these researchers used only one software platform for their study, i.e. " talent platform pymetrics"

As if elite professors have no idea how the labor market works in reality they recommend that "applicants would need to apply widely". Pure common sense and practice!

"In brief

• Many companies use AI-powered tools to sort job applications. There has been hope that these tools reduce bias, compared to assessment of applications by humans alone.
• Through analyzing a dataset of 4 million applications from an AI-based screening tool, researchers found bias against Black and Asian candidates.
• In some cases, the AI hiring tools rejected a candidate from several jobs at a rate higher than would be expected if the candidate was being assessed by each company individually – an outcome they called “systemic rejection.”
• The researchers don’t yet know why these tools are biased. They argue that this work supports the need for more transparency into how AI hiring tools work, and they note that companies are still responsible for checking for biases in their hiring tools and processes.

..."

From the abstract:

"Many employers screen job applicants with algorithms built by the same few algorithm vendors.

We hypothesize that algorithmic monoculture leads to the same individuals and members of the same racial groups facing rejection.

We acquire and analyze a novel dataset of 3 million applicants submitting 4 million applications where all the applications are screened by algorithms built by the same vendor.

We find clear racial disparities in applicant outcomes. Of all applications submitted by Asian and Black applicants, 14.74% and 25.87% are submitted to positions that adversely impact Asian and Black applicants [???], respectively, according to U.S. employment discrimination standards.

Individuals also receive homogeneous outcomes: 4% of all applicants who apply to 10 positions are recommended for rejection from all positions, a rate higher than expected by chance [???].

To better understand this homogeneity, we leverage the deterministic replicability of hiring algorithms to generate the outcomes applicants would have received if they applied to all positions.

We show that applicants would need to apply widely in order to ensure their applications are considered by a human"

AI hiring tools show racial bias | Stanford Report "For many job postings, AI screening tools recommend white candidates at higher rates than Black and Asian candidates, new research shows."

Algorithmic Monocultures in Hiring (preprint, open access)

Notice "appeared to be biased"