---
title: Bias and fairness
description: 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 {: #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](cross-acc)  | Measure the model's accuracy for each class segment of the protected feature. | Validation data
[Cross-Class Data Disparity](cross-data) | Depict why a model is biased, and where in the training data it learned that bias from. | Validation data
[Per-Class Bias](per-class)  | Identify if a model is biased, and if so, how much and who it's biased towards or against. | Validation data
[Settings](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) | 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](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) in **Bias and Fairness** > **Settings**.

See the [Bias and Fairness reference](bias-ref) 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 {: #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.
