An open platform for accelerating the development of eXplainable AI systems
Description of our repositories:
- mlxplain: Comprehensive framework encompassing provided algorithms.
- rules-extraction: High-level explanation algorithms, comprising of three approaches: rule extraction, local identification of relevant patterns, and generation of global meaningful patterns, in addition to the development of a novel algorithm combining these approaches to create comprehensive explanations.
- dimlpfidex: Low level explanation algorithms, extracting propositional rules from deep models where theprecise location of axis-parallel hyperplanes is known. Two new algorithms are presented, one for local explanation and the other for global explanations.
- fuge: Algorithm to construct fuzzy systems that predict human decision-making outcomes and provide understandable explanations.
- notebooks: Use cases in the form of Jupyter notebooks, and related Docker files for easy deployment.
- docker-notebook-base: Docker base image for the notebook, comprising of Jupyter and all dependency packages necessary to run the provided notebooks.
- huggingface.co/HES-XPLAIN: Storage of our pre-trained models and datasets.
- hes-xplain.github.io: Website for HES-XPLAIN, where general information, use case notebooks, documentations and download are provided.