awesome-machine-learning-interpretability
A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources.
If you want to contribute to this list (and please do!) read over the contribution guidelines, send a pull request, or contact me @jpatrickhall.
An incomplete, imperfect blueprint for a more human-centered, lower-risk machine learning. The resources in this repository can be used to do many of these things today. The resources in this repository should not be considered legal compliance advice.
Image credit: H2O.ai Machine Learning Interpretability team, https://github.com/h2oai/mli-resources.
Explainability- or Fairness-Enhancing Software Packages
Interpretable ("Whitebox") or Fair Modeling Packages
Model Interpretation series by Dipanjan (DJ) Sarkar:
Grad-CAM (GitHub topic)
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
Debugging Machine Learning Models (ICLR workshop proceedings)
Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
U.K. Information Commissioner's Office (ICO) AI Audting Framework (overview series)
Warning Signs: The Future of Privacy and Security in an Age of Machine Learning
You Created A Machine Learning Application Now Make Sure It's Secure
A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
A Marauder’s Map of Security and Privacy in Machine Learning
Explaining Explanations: An Overview of Interpretability of Machine Learning
Interpretable Machine Learning: Definitions, Methods, and Applications
On the Responsibility of Technologists: A Prologue and Primer
Please Stop Explaining Black Box Models for High-Stakes Decisions
Towards A Rigorous Science of Interpretable Machine Learning
H2O-3
Scikit-learn
H2O-3
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