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@h2oai @the-george-washington-university @bnh-ai

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  1. A curated list of awesome machine learning interpretability resources.

    1.8k 321

  2. Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

    Jupyter Notebook 464 152

  3. Materials for GWU DNSC 6279 and DNSC 6290.

    Jupyter Notebook 206 159

  4. Practical ideas on securing machine learning models

    TeX 21 4

  5. Paper and talk from KDD 2019 XAI Workshop

    TeX 16 2

  6. Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

    TeX 13 4

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