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Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings.
SR 11-7/OCC11-12: Supervisory Guidance on Model Risk Management (by Federal Reserve Board and Office of the Comptroller of the Currency)
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" Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 1135–1144
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Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning . PMLR, 5491–5500
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Pitfalls to avoid when interpreting machine learning models
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Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society . 180–186
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
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Unwrapping the black box of deep ReLU networks: interpretability, diagnostics, and simplification
Agus Sudjianto, William Knauth, Rahul Singh, Zebin Yang, and Aijun Zhang. 2020 · 2020
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Regulation of artificial intelligence (in Wikipedia)
2021 · 2021
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Request for information and comment on financial institutions’ use of artificial intelligence, including machine learning (by US Agencies)
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Single-index model tree
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Comptroller’s Handbook on Model Risk Management (by US Office of the Comptroller of the Currency)
2021 · 2021
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European Union’s Artificial Intelligence Act (by European Commission)
2021 · 2021
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Zebin Yang, Aijun Zhang, and Agus Sudjianto. 2021b · 2021
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