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Frontiers | Principles and Practice of Explainable Machine Learning

frontiersin.org · 1,104 words · saved by 1 readers

Citation numbers are available from Dimensions University of Ottawa, Canada University of Pavia, Italy Tennessee Technological University, United States Ioannis Papantonis and Vaishak Belle Shruti Kaushik, Abhinav Choudhury, Pankaj Kumar Sheron, Nataraj Dasgupta, Sayee Natarajan, Larry A. Pickett and Varun Dutt Vladimir Medved, Sara Medved and Ida Kovač Alexandra Olteanu, Carlos Castillo, Fernando Diaz and Emre Kıcıman Kristian Kersting Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods—machine lea

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