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GitHub projects from awesome lists
Search names, descriptions, topics, tags, and stacks, then tune results by ecosystem, freshness, health, and cross-list signal.
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Promote a GitHub repo at the top of Awesome repository list views for 7 days.
Fit interpretable models. Explain blackbox machine learning.
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.
Interpretability and explainability of data and machine learning models
moDel Agnostic Language for Exploration and eXplanation
Semantica • Build AI systems that can explain, trace, and justify every decision. Knowledge graphs, context graphs, reasoning engines, provenance, and governance for production AI.
XAI - An eXplainability toolbox for machine learning
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University