COMPAS Analysis Using Aequitas vs Fairlearn

Side-by-side — pricing, strengths, tradeoffs, and fit.

COMPAS Analysis Using Aequitas

Fairness analysis for machine learning models using COMPAS dataset

Fairlearn

Assess and improve fairness in machine learning models.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Fairness analysis for machine learning models using COMPAS dataset

One-liner

Assess and improve fairness in machine learning models.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best 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

✓ Best for

Teams needing to ensure fairness in their machine learning models for compliance or ethical reasons
Developers working on sensitive applications where bias can have significant social impact

Not ideal for

✕ Not for

Projects requiring real-time fairness analysis
Applications that do not involve the COMPAS dataset or similar structured data

✕ Not for

Projects that do not require fairness assessment and mitigation
Applications where the primary focus is on model performance rather than fairness

Strengths

↑ Strengths

Bias detection and mitigation using the COMPAS dataset
Visualization of fairness metrics
Detailed analysis reports for model evaluation

↑ Strengths

Bias mitigation techniques
Fairness metrics calculation
Model comparison for fairness
Integration with popular ML frameworks

Weaknesses

↓ Weaknesses

Limited dataset specificity
Complex setup and configuration
Documentation lacks depth and examples
Performance issues with large datasets

↓ Weaknesses

Limited language support restricts multi-language projects
Complex setup process can hinder quick adoption
Documentation lacks depth on advanced usage scenarios
Performance overhead when applying bias mitigation techniques
Full COMPAS Analysis Using Aequitas profile →Full Fairlearn profile →
COMPAS Analysis Using Aequitas vs Fairlearn | AI Navigator