# Bias and fairness
URL: https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/index.html

> Introduces the Bias and Fairness tabs, which identify if a model is biased and why the model is learning bias from the training data.

# 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](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation-post-autopilot) in Bias and Fairness > Settings.

See the [Bias and Fairness reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html) 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.

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