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Modeling > Model insights > Bias and Fairness

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:

Leaderboard tab Description Source
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.
Updated August 23, 2023