# Explainability Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/explainability
> Markdown URL: https://aitinkerers.org/technologies/explainability.md
> Technology record last updated: 2026-03-25T12:19:46Z
> Generated: 2026-10-23T15:53:34Z

Explainability transforms black-box AI into transparent systems by mapping how specific inputs drive model decisions.

Modern machine learning often operates in a vacuum, but tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) break that cycle. These frameworks provide feature importance scores to show exactly why a credit risk model denied a loan or why a medical vision system flagged a specific scan. By quantifying the contribution of every variable, teams move from blind trust to audit-ready reliability. This isn't just about compliance; it is about debugging high-stakes deployments where a 1% shift in data distribution can derail an entire pipeline.

- Official technology site: https://christophm.github.io/interpretable-ml-book/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [elluminate](https://paris.aitinkerers.org/talks/rsvp_uDUj04o6Hs0)

- Event context: AI Tinkerers - Paris Inaugural Meetup featuring Mistral AI — 2024-09-03 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_uDUj04o6Hs0

## Related Technologies

- [Elluminate](https://aitinkerers.org/technologies/elluminate) ([Markdown](https://aitinkerers.org/technologies/elluminate.md)) — 4 public demos
- [Evaluations](https://aitinkerers.org/technologies/evaluations) ([Markdown](https://aitinkerers.org/technologies/evaluations.md)) — 3 public demos
- [LLM](https://aitinkerers.org/technologies/llm) ([Markdown](https://aitinkerers.org/technologies/llm.md)) — 123 public demos