AI engineering glossary · AI ethics law product
What is bias mitigation?
Bias mitigation (also called debiasing): The set of techniques used to reduce unfair behaviour in a machine learning system. They are applied before training by fixing the data, during training by changing the objective, or after training by adjusting the outputs. Each usually trades some accuracy for fairness.
Bias mitigation: an example
A team reweights training rows so an under-represented group counts more, then checks that approval-rate gaps shrink without accuracy falling much.
Related terms
- Reweighing
- Adversarial debiasing
- Post-processing fairness
- Fairness metric
More AI ethics law product terms
- Black-box model
- Conformity assessment
- Consent
- Consent Manager
- Contestability
- Copyright and AI training data
- Cost per resolved query
- Counterfactual explanation
Where you learn it
Bias mitigation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.