AIF360

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AIF360 is an Open Source & Models tool that helps detect and mitigate bias in machine learning models and datasets. It provides a comprehensive set of fairness metrics and algorithms for the AI application lifecycle.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is AIF360?

AIF360 is an extensible open-source library designed to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. Available in both Python and R, it offers a comprehensive set of fairness metrics for datasets and models, along with explanations for these metrics. The toolkit also includes various algorithms to mitigate bias, translating algorithmic research into practical applications across diverse domains such as finance, human capital management, healthcare, and education. It is developed with extensibility in mind, encouraging contributions of new metrics, explainers, and debiasing algorithms from the research community.

Best used for

Ideal for developers and AI researchers who need to identify and reduce bias in their machine learning models, evaluate the fairness of datasets, and implement bias mitigation strategies. Especially valuable for ensuring ethical AI development in sensitive domains like finance, healthcare, and human resources.

Common actions

detect bias

mitigate bias

evaluate fairness

develop fair AI

analyze datasets

low-code/no-codeopen-sourceworkflowsautomated workflowgithub copilotcollaborationface swappingdeepfake"AI Agents"

Capabilities

Key features

Fairness metrics
Bias mitigation algorithms
Python and R support
Interactive experience
Extensible library
Tutorials and demos

Target Audience

developerAI researcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages does AIF360 support?

AIF360 is available as an open-source library in both Python and R. This allows developers and researchers to integrate its fairness metrics and bias mitigation algorithms into their preferred data science environments.

What types of bias can AIF360 help detect and mitigate?

AIF360 provides a comprehensive set of group fairness metrics derived from selection rates and error rates, including rich subgroup fairness. It also offers algorithms to mitigate various forms of bias in both datasets and machine learning models.

Are there examples or tutorials available to help users get started with AIF360?

Yes, AIF360 includes an interactive experience for a gentle introduction, along with tutorials and other Jupyter notebooks that offer a deeper, data scientist-oriented introduction. The examples directory contains a diverse collection of working code.

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