AIF360
Fairness metrics for datasets and machine learning models.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Verified · Jul 12, 2026Overview
What is AIF360?
A comprehensive set of fairness metrics to evaluate the bias in datasets and machine learning models, crucial for ensuring ethical AI practices.
Key differentiator
“The only open-source library that provides a wide range of fairness metrics and tools for both datasets and machine learning models.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary focus on tabular datasets, limited functionality for text or image data
Evaluation of fairness metrics can be computationally expensive and slow for large-scale data
Fit analysis
Who is it for?
✓ Best for
Teams needing to ensure their ML models are fair and unbiased
Organizations that require detailed fairness metrics for regulatory compliance
✕ Not a fit for
Projects where real-time bias detection is required (AIF360 is primarily a post-training evaluation tool)
Applications with very limited computational resources, as AIF360 can be resource-intensive
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with AIF360
Step-by-step setup guide with code examples and common gotchas.