Fairlearn

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Fairlearn is an Open Source & Models tool that helps developers assess and improve the fairness of machine learning models. It provides mitigation algorithms and metrics for model assessment.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is fairlearn?

Fairlearn is a Python package designed to empower developers of artificial intelligence (AI) systems to assess and mitigate unfairness in their machine learning models. It offers a comprehensive suite of tools, including metrics for identifying groups negatively impacted by a model and algorithms for mitigating unfairness across various AI tasks and fairness definitions. The package focuses on addressing allocation harms (e.g., in hiring or lending) and quality-of-service harms, following a group fairness approach. Fairlearn enables users to compare multiple models based on fairness and accuracy metrics, providing a robust framework for responsible AI development. It is open-source and includes Jupyter notebooks for practical usage examples.

Best used for

Ideal for developers and data scientists who need to ensure their machine learning models are fair and unbiased, identify and address allocation or quality-of-service harms, and compare models based on fairness and accuracy. Especially valuable for building responsible AI systems in sensitive domains like hiring or lending.

Common actions

assess fairness

mitigate bias

improve AI ethics

evaluate models

deepfakeworkflowslow-code/no-codeautomated workflowcollaborationopen-source"AI Agents"github copilotface swapping

Capabilities

Key features

Assess model fairness
Mitigate unfairness
Fairness metrics
Mitigation algorithms
Group fairness analysis
Model comparison

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of unfairness does Fairlearn address?

Fairlearn primarily addresses two types of harms: allocation harms, which occur when AI systems extend or withhold opportunities or resources (e.g., in hiring or lending), and quality-of-service harms, where a system may not perform equally well for all individuals.

How does Fairlearn define fairness?

Fairlearn follows the approach of group fairness, which involves identifying groups of individuals at risk for experiencing harms. It formalizes fairness through constraints that require comparable AI system behavior across these specified groups, enabling assessment and mitigation under various definitions.

What resources are available for learning Fairlearn?

Users can get started with Fairlearn by installing it via pip and visiting the Quickstart guide. Comprehensive learning resources include a user guide, example notebooks, an example gallery, and API reference. Community support is available via Stack Overflow and Discord.

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