AI engineering glossary · AI ethics law product
What is historical bias?
Historical bias: Unfairness that already exists in the real world and is faithfully captured in the data, so a model trained on that data reproduces it even when sampling and measurement are perfect. It shows that accurate data can still lead to harmful predictions if the past itself was unjust.
Historical bias: an example
A model trained on decades of loan approvals in which certain communities were rarely approved learns to keep rejecting applicants from those pin codes.
Related terms
More AI ethics law product terms
- Homomorphic encryption
- Human oversight
- Implicit feedback
- Individual fairness
- Interpretability
- k-anonymity
- Label bias
- Lawful basis
Where you learn it
Historical bias is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.