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CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.
Features
- Counterfactual Explanations and Algorithmic Recourse in Julia
- Machine learning models like Deep Neural Networks have become so complex, opaque and underspecified in the data that they are generally considered Black Boxes
- Documentation available
- Examples available
- Implemented Counterfactual Generators
Project Samples
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Data VisualizationLicense
MIT LicenseFollow CounterfactualExplanations.jl
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