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Equalized odds

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TL;DR. A fairness metric where true positive rate and false negative rate are equal across all groups.

Technical Definition

A fairness metric where true positive rate and false negative rate are equal across all groups.

How it works

Equalized odds assesses model fairness by ensuring that the true positive rate and false negative rate are the same for all subgroups defined by sensitive attributes. This goes beyond simply ensuring equal performance for the positive class, aiming for balanced errors across both positive and negative outcomes for every group.

Related Concepts

  • Demographic parity — A fairness metric ensuring prediction rates are the same across different demographic groups.
  • Equality of opportunity — A fairness metric ensuring a model's positive predictions are equally accurate across different sensitive groups.
  • Fairness metric — A quantifiable measure used to assess the fairness of a model's predictions across different groups.

Further Reading

  • Google ML Glossary