AI Fairness 360

Open source

Open source library for detecting and mitigating bias in machine learning datasets and models.

by The Linux FoundationOfficial site ↗Source code ↗

Pricing: free
Free tier: true
License: open-source
Open source: Yes

Editorial review

AI Fairness 360 has not yet been editorially reviewed. User reviews and published field data below are unaffected — we say so rather than implying an editorial verdict.

Facts & attributes

Every value carries its evidence state. “—” means unknown — never a silent default (P-02).

Identity · Taxonomy

Documentation(HTTPS URL): https://aif360.readthedocs.io/en/latest/Verified
Tags(open-source;cloud-native): fairness, bias detection, bias mitigation, machine learning, responsible AI, Python, RVerification pending

Platform · Deployment

Delivery model(SaaS;self-hosted;desktop;mobile;embedded;managed-cloud;appliance): self-hostedVerification pending

Pricing · Licensing

Free plan(true/false): trueVerification pending
License model(proprietary;open-source;source-available;dual-license): open-sourceVerification pending
Pricing model(free;subscription;usage;seat;asset;transaction;revenue-share;perpetual;custom): freeVerification pending
Source repository(HTTPS URL): https://github.com/Trusted-AI/AIF360Verified
SPDX license IDs(Apache-2.0;MIT): Apache-2.0Verified

Features · Product · Fit

Core capabilities(authoring;automation;analytics): metrics for datasets and models to test for biases, explanations for these metrics, algorithms to mitigate bias in datasets and models, bias mitigation algorithms such as Optimized Preprocessing, Disparate Impact Remover, and Adversarial Debiasing, comprehensive set of group fairness metrics derived from selection rates and error rates, packages available in both Python and RVerification pending

Api · Integrations · Ecosystem

API available(true/false): trueVerification pending

Audience · Use · Case

Best for(Neutral editorial summary): Data scientists who need fairness metrics and bias-mitigation algorithms in Python or R machine learning pipelines.Verification pending
Primary use cases(build;monitor;automate): detect and mitigate bias in machine learning models throughout the AI application lifecycle, auditing dataset and model fairness in domains such as finance, human capital management, healthcare, and education, translating algorithmic fairness research into actual practiceVerification pending
Skill level(beginner;intermediate;advanced;specialist): specialistVerification pending

Lifecycle · Versioning

Current version(1.12.3): 0.6.1Verified
Maintenance status(active;maintenance;deprecated;EOL;archived): activeVerification pending

Vendor · Maintainer

Founded year(2012): 2018Verified

User reviews

Review policy

No published reviews yet.