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  1. Contrastive Explanation.Peter Lipton - 1990 - Royal Institute of Philosophy Supplement 27:247-266.
    According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...)
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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.
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  • Peeking inside the black-box: A survey on explainable artificial intelligence (XAI).A. Adadi & M. Berrada - 2018 - IEEE Access 6.
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  • Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.
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  • (1 other version)Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems.Michelene T. H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann & Robert Glaser - 1989 - Cognitive Science 13 (2):145-182.
    The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...)
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  • Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management.Joseph E. Mercado, Michael A. Rupp, Jessie Y. C. Chen, Michael J. Barnes, Daniel Barber & Katelyn Procci - 2016 - Human Factors: The Journal of the Human Factors and Ergonomics Society 58 (3):401-415.
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  • (1 other version)Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems.Michelene T. H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann & Robert Glaser - 1989 - Cognitive Science 13 (2):145-182.
    The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...)
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  • Causability and explainability of artificial intelligence in medicine.Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal & Heimo Müller - 2019 - Wires Data Mining and Knowledge Discovery 9 (4):e1312.
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  • A Survey of Methods for Explaining Black Box Models.Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti & Dino Pedreschi - 2019 - ACM Computing Surveys 51 (5):1-42.
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