Bias in AI Hiring Systems
The effects of bias and discrimination within new AI hiring systems on marginalized communities.
This research paper raises awareness about how people of color are being discriminated against by AI hiring systems. It talks about the effects, causes, and solutions to the issue. Research also includes an overview of different machine learning algorithms, such as Gradient Boosting, HistGBM, and neural networks, used to replicate AI hiring models to simulate bias.
The argument leans on two real studies: the Bertrand & Mullainathan 2004 labor-market audit (the original "Jamal vs. Greg" résumé experiment) and a 2025 Brookings analysis showing that 98.4% of Fortune 500 companies now use AI somewhere in hiring.