# jphall663/awesome-machine-learning-interpretability

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4,044 stars · 630 forks · CC0-1.0

## Links

- GitHub: https://github.com/jphall663/awesome-machine-learning-interpretability
- awesome-repositories: https://awesome-repositories.com/repository/jphall663-awesome-machine-learning-interpretability.md

## Topics

`ai-safety` `awesome` `awesome-list` `data-science` `explainable-ml` `fairness` `interpretability` `interpretable-ai` `interpretable-machine-learning` `interpretable-ml` `machine-learning` `machine-learning-interpretability` `privacy-enhancing-technologies` `privacy-preserving-machine-learning` `python` `r` `reliable-ai` `secure-ml` `transparency` `xai`

## Description

A curated list of awesome responsible machine learning resources.

## Tags

### Repository Format

- [Awesome List](https://awesome-repositories.com/f/repository-format/awesome-list.md) — A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.

### Part of an Awesome List

- [Applied Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/applied-machine-learning.md) — Frameworks and code for interpreting machine learning models.
- [Curated Research Lists](https://awesome-repositories.com/f/awesome-lists/ai/curated-research-lists.md) — Tools and techniques for model transparency.
- [Awesome Lists](https://awesome-repositories.com/f/awesome-lists/more/awesome-lists.md) — ML interpretability.
- [Related Research Collections](https://awesome-repositories.com/f/awesome-lists/more/related-research-collections.md) — Curated list of resources for machine learning interpretability.