How do we eliminate bias and discrimination in AI training data?
Reviewed by Jason Burns, Editorial Steward · Last updated
You cannot fully eliminate bias — training data reflects the world — but you can reduce and manage it by auditing datasets, balancing representation, documenting data provenance, testing outputs across demographic slices, and building human review into high-stakes decisions. As Fei-Fei Li, put it on the record: "There's nothing artificial about AI. It's inspired by people, it's created by people, and—most importantly—it impacts people."