Aequitas vs Alibi
A side-by-side comparison of two Explainability and Fairness AI agents — to help you pick the right one.
Aequitas
Aequitas is an open-source bias audit toolkit designed to evaluate machine learning models for discrimination and fairness. It provides statistical metrics and visualizations to assess disparities in model outcomes across demographic groups. The tool helps users identify and mitigate bias in predictive risk-assessment systems.
Alibi
Alibi is an open-source Python library designed for interpreting and explaining machine learning models, with a focus on black-box model explanations. It provides tools for model inspection, fairness evaluation, and instance-based explanations to enhance transparency.
| Aequitas | Alibi | |
|---|---|---|
| Category | Explainability and Fairness | Explainability and Fairness |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
Aequitas: what it solves
It detects and quantifies bias in machine learning models, enabling developers and policymakers to address unfair disparities in algorithmic decision-making.
Alibi: what it solves
It helps users understand how machine learning models make predictions and assess potential biases, enabling better trust and accountability in AI systems.