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- Interpretable Machine Learning – A Guide for Making Black Box Models Explainable – Christoph Molnar – Link
- Interpretable AI – Building explainable machine learning systems, Ajay Thampi – Link
Papers
- Multi-Objective Counterfactual Explanations by Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl – Link
- Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers by Divyat Mahajan, Chenhao Tan, Amit Sharma – Link
- Counterfactual Explanations for Machine Learning: A Review by Sahil Verma, John Dickerson, Keegan Hines – Link
