fairness

nber.org

Free

When Is Discrimination Unfair? (NBER Working Paper 30236)

Authors

Peter Kuhn, Trevor Osaki

Venue

NBER Working Paper 30236

Published

July 2022

Peter Kuhn and Trevor Osaki survey people on which kinds of hiring and pay discrimination they consider unfair, and why.

Why I recommend it: A working paper based on survey attitudes — it tells you what people think is fair, not what the law allows.

nber.orgAdded Sep 24, 20260 opens

GitHub

Free

AI Fairness 360

An open-source library of metrics and algorithms for finding and reducing unwanted bias in datasets and models, in Python and R. Free.

From the site: A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. - Trusted-AI/AIF360

Why I recommend it: For the harm that shows up in ordinary systems long before anything dramatic does — hiring screens, lending, scoring. Measuring bias is the easy half; deciding what fair means is yours.

GitHubAdded Sep 17, 20260 opens

Algorithmic Monocultures in Hiring (FAccT 2026)

Venue

FAccT 2026 (ACM Conference on Fairness, Accountability, and Transparency)

Published

2026

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.