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Algorithmic Monocultures in Hiring (FAccT 2026)
The PDF holds this page's summary, my note and the link — one page you can keep or print.
What it is
Stanford-led study of 3 million applicants screened by a single algorithm vendor, finding racial disparities and homogeneous rejections — the same people get screened out everywhere. Explains why applicants must apply widely to reach a human.
In plain terms
This research study examines how automated screening tools used by multiple employers cause repeated rejections and racial disparities. Use this paper to understand how hiring algorithms work and why applying to more jobs helps you reach a human reviewer.
Why I recommend it
This is the evidence behind advice I give constantly: one rejection is often the same algorithm repeated, not a verdict on you.
Created by Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang (Stanford HAI)
Topics
Added Aug 29, 2026 · 0 opens