Bias and Fairness¶
The Bias and Fairness tabs identify if a model is biased and why the model is learning bias from the training data. The following sections provide additional information on using the tabs:
|Cross-Class Accuracy||Measure the model's accuracy for each class segment of the protected feature.||Validation data|
|Cross-Class Data Disparity||Depict why a model is biased, and where in the training data it learned that bias from.||Validation data|
|Per-Class Bias||Identify if a model is biased, and if so, how much and who it's biased towards or against.||Validation data|
|Settings||Configure fairness tests from the Leaderboard.||N/A|
If you did not configure Bias and Fairness prior to model building, you can configure fairness tests for Leaderboard models in Bias and Fairness > Settings.
See the Bias and Fairness reference for a description of the methods used to calculate fairness for a machine learning model and to identify any biases from the model's predictive behavior.
Bias and Fairness considerations¶
Consider the following when using the Bias and Fairness tab:
- Bias and fairness testing is only available for binary classification projects.
- Protected features must be categorical features in the dataset.