# jphall663/awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

Repository: https://github.com/jphall663/awesome-machine-learning-interpretability
Canonical: https://ross.abutalabs.com/products/awesome-machine-learning-interpretability
License: CC0-1.0
License Family: permissive
Topics: fairness, xai, interpretability, transparency, machine-learning, data-science, python, r, awesome, awesome-list, machine-learning-interpretability, interpretable-machine-learning, interpretable-ml, interpretable-ai, explainable-ml, ai-safety, privacy-enhancing-technologies, privacy-preserving-machine-learning, reliable-ai, secure-ml
Last push: 2026-06-03T15:17:31+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 85, release rhythm 35, longevity 100
- inputs: {"age_days": 2995, "days_push": 91, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4060, forks 629 (observed 2026-08-28T04:08:33.997672+00:00)

## What it is
A curated awesome-list of resources on machine learning interpretability and responsible AI, including fairness, explainability, transparency, and privacy-preserving ML. The collection has been reorganized and moved to the HallResearch.ai Library, with this repo preserved as a legacy archive.

## Use cases
- find resources on explainable machine learning
- learn about model interpretability techniques
- study fairness and bias in machine learning
- find privacy-preserving machine learning papers and tools
- research responsible AI and AI governance materials
- find interpretable ML resources for Python and R

## When to choose
- you want a curated starting point for explainable and responsible ML topics
- you need links spanning fairness, transparency, and AI safety
- you want a CC0-licensed resource list you can freely reuse

## When to avoid
- you need actively maintained content in this repo itself - updates happen in the HallResearch.ai Library
- you need a software tool or library rather than a reading list
- you need in-depth tutorials rather than curated links

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, security, privacy, documentation
- domain: machine-learning, artificial-intelligence, data-science, awesome-lists, tutorials
- platform: python, cross-platform
- tags: awesome-list, explainable-ai, xai, interpretability, fairness, responsible-ai, ai-governance, ai-safety, curated-resources

## Member repositories
- jphall663/awesome-machine-learning-interpretability (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:33.997672+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:35.737355+00:00, confidence not recorded.
  - readme: https://github.com/jphall663/awesome-machine-learning-interpretability (fetched 2026-08-28T04:08:33.997672+00:00, sha a739ae1144d3)
- Data as of 2026-08-30T08:39:29.467469+00:00.