explainability

CodeBoarding

+38

Interactive architecture diagrams for codebases

awesome-production-machine-learning

+32

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

attention-span

+22

Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.

shap/shap

+13

A game theoretic approach to explain the output of any machine learning model.

interpret

+3

Fit interpretable models. Explain blackbox machine learning.

responsible-ai-toolbox

+2

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.