How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Training PyTorch models with differential privacy
Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu
Source code/webpage/demos for the What-If Tool
A Python package to assess and improve fairness of machine learning models.
The main features of fairlearn/fairlearn are: Explainability and Fairness, Model Fairness And Privacy, Guardrails and AI Safety.
Projects with overlapping indexed features include: trusted-ai/aif360 — A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and… seldonio/alibi — Algorithms for explaining machine learning models. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… pair-code/what-if-tool — Source code/webpage/demos for the What-If Tool. pytorch/opacus — Training PyTorch models with differential privacy. facebookresearch/crypten — A framework for Privacy Preserving Machine Learning.