Aequitas vs captum
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.
captum
Captum is a model interpretability library for PyTorch that provides tools to understand and debug deep learning models. It includes algorithms like integrated gradients, saliency maps, and SmoothGrad to analyze feature importance and model behavior.
| Aequitas | captum | |
|---|---|---|
| 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.
captum: what it solves
It helps researchers and developers interpret PyTorch models by revealing how input features influence predictions, improving transparency and trust in AI systems.