Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
Free GitHub version
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- 2026-10-10
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AI Explainability 360 github.com
Interpretability and explainability of data and machine learning models.
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InterpretML github.com
InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box…
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L2X github.com
Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
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Explainable Machine learning in Credit Risk Management papers.ssrn.com
Proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms.
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Beneficial and harmful explanatory machine learning link.springer.com
Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…
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