# Bias and fairness

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

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.579259+00:00` (UTC).

## Primary page

- [Bias and fairness](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/index.html): Full documentation for this topic (HTML).

## Sections on this page

- [Bias and Fairness considerations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/index.html#bias-and-fairness-considerations): In-page section heading.

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [Modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/index.html): Linked from this page.
- [Model insights](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html): Linked from this page.
- [Cross-Class Accuracy](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-acc.html): Linked from this page.
- [Cross-Class Data Disparity](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-data.html): Linked from this page.
- [Per-Class Bias](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/per-class.html): Linked from this page.
- [Settings](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation-post-autopilot): Linked from this page.
- [Bias and Fairness reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html): Linked from this page.

## Documentation content

# 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.