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As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable.
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Antorán, J., Bhatt, U., Adel, T., Weller, A., and Hernández-Lobato, J. M · 2020
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Explainable machine learning in deployment
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On network science and mutual information for explaining deep neural networks
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You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods
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Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?, 2020
Hase, P. and Bansal, M · 2020
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
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Explanation-based tuning of opaque machine learners with application to paper recommendation
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Slack, D., Hilgard, S., Jia, E., Singh, S., and Lakkaraju, H · 2020
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Zhang, Y., Liao, Q. V., and Bellamy, R. K. E · 2020
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Different “intelligibility” for different folks
Zhou, Y. and Danks, D · 2020
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