Study: AI hiring tools may form new biases beyond training data

New research summarized by MIT Technology Review finds that large language models used to screen job applicants don't just absorb bias from their training data — they can also develop additional biases of their own. The finding challenges the common assumption that cleaning up training data is enough to make AI hiring tools fair.

Why it matters: AI resume screening is already widespread, and regulators in the EU and several US states are starting to require bias audits for hiring algorithms. If models generate bias independent of their training data, audits that only check data provenance won't catch it, raising the bar for what a legitimate fairness audit needs to test.