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What Is Explainable AI Explainable vs Interpretable Machine Learning
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The first 1000 people to use the link will get a free trial of Skillshare Premium Membership: https://skl.sh/jordanharrod09205 • Can we explain how machine learning models make decisions? And what does it mean to explain a model anyway? • Twitter - / jordanbharrod • Instagram - / jordanbharrod • Sources: • Montavon, G., Samek, W., Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing: A Review Journal, 73, 1–15. https://doi.org/10.1016/j.dsp.2017.10... • Doran, D., Schulz, S., Besold, T. R. (2018). What does explainable AI really mean? A new conceptualization of perspectives. CEUR Workshop Proceedings, 2071. • Roscher, R., Bohn, B., Duarte, M. F., Garcke, J. (2020). Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access, 8, 42200–42216. https://doi.org/10.1109/ACCESS.2020.2... • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-00... • Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., Müller, K.-R. (2020). Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond, 1–24. Retrieved from http://arxiv.org/abs/2003.07631 • Agarwal, R., Frosst, N., Zhang, X., Caruana, R., Hinton, G. E. (2020). Neural Additive Models: Interpretable Machine Learning with Neural Nets. Retrieved from http://arxiv.org/abs/2004.13912 • Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116 • Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116 • Samek, W., Wiegand, T., Müller, K.-R. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. Retrieved from http://arxiv.org/abs/1708.08296 • Adadi, A., Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2... • Rai, A. (2020). Explainable AI: from black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00... • Mozannar, H., Sontag, D. (2020). Consistent Estimators for Learning to Defer to an Expert. Retrieved from http://arxiv.org/abs/2006.01862 • Gopinath, D., Agrawal, M., Murray, L., Horng, S., Karger, D., Sontag, D. (2020). Fast, Structured Clinical Documentation via Contextual Autocomplete, 1–25. Retrieved from http://arxiv.org/abs/2007.15153 • Mittelstadt, B., Russell, C., Wachter, S. (2019). Explaining explanations in AI. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, 279–288. https://doi.org/10.1145/3287560.3287574
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