COMPAS Analysis Using Aequitas
Fairness analysis for machine learning models using COMPAS dataset
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is COMPAS Analysis Using Aequitas?
A tool to analyze and mitigate bias in machine learning models, specifically using the COMPAS recidivism prediction dataset. It helps ensure fairness and transparency in AI decision-making processes.
Key differentiator
“The only tool offering detailed fairness analysis specifically tailored for the COMPAS recidivism prediction dataset, providing a unique angle on algorithmic bias.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The tool is specifically designed for the COMPAS recidivism prediction dataset, limiting its applicability to other datasets or domains.
Setting up the environment requires a detailed understanding of Python dependencies and specific library versions, which can be time-consuming for new users.
The documentation provides basic usage instructions but lacks comprehensive tutorials or real-world examples to guide users through complex scenarios.
Processing time increases significantly when analyzing large datasets, which can slow down the analysis and mitigation processes.
Fit analysis
Who is it for?
✓ Best for
Researchers studying algorithmic fairness using the COMPAS dataset
Teams needing detailed analysis of model biases in recidivism prediction
Educators teaching about machine learning ethics and bias
✕ Not a fit for
Projects requiring real-time fairness analysis
Applications that do not involve the COMPAS dataset or similar structured data
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
Next step
Get Started with COMPAS Analysis Using Aequitas
Step-by-step setup guide with code examples and common gotchas.