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We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to decisions produced by classification models.
1901
Earlier work this paper cites.
Karimi,A. H., Barthe, G., Balle, B. and Valera, I. Model-Agnostic Counterfactual Explanations for Consequential Decisions. Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. arXiv: 1905.11190
1905
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Giannotti, F., Greco, S., Sacca, D. and Zaniolo, C. Programming with Non-Determinism in Deductive Databases. Annals of Mathematics in Artificial Intelligence
1997
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Responsibility and Blame: A Structural-Model Approach
Chockler, H. and Halpern, J. Y · 2004
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Causes and Explanations: A Structural-Model Approach: Part 1
Halpern, J. and Pearl, J · 2005
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Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Koch, C., Mateis, C., Perri, S. and Scarcello, F. The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic
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Baral, C., Gelfond, M. and Rushton, N. Probabilistic Reasoning with Answer Sets. Theory and Practice of Logic Programming
2009
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Pearl, J. Causality: Models, Reasoning and Inference
2009
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Caniupan, M. and Bertossi, L. The Consistency Extractor System: Answer Set Programs for Consistent Query Answering in Databases. Data & Knowledge Engineering
2010
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The Complexity of Causality and Responsibility for Query Answers and Non-Answers
Meliou, A., Gatterbauer, W., Moore, K. F. and Suciu, D · 2010
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Gebser, M., Kaminski, R. and Schaub, T. Complex Optimization in Answer Set Programming. Theory and Practice of Logic Programming
2011
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Flach, P. Machine Learning
2012
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Martens, D. and Provost, F. J. Explaining Data-Driven Document Classifications. MIS Quarterly
2014
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Lundberg, S. and Lee, S.-I. A Unified Approach to Interpreting Model Predictions. Proc. NIPS 2017, pp. 4765-4774
2017
Later among the works it cites.
2017
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Bertossi, L. Characterizing and Computing Causes for Query Answers in Databases from Database Repairs and Repair Programs. Proc. FoIKs, 2018, Springer LNCS 10833, pp. 55-76. Revised and extended version as Corr Arxiv Paper cs.DB/1712.01001
2018
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Eiter, T., Germano, S., Ianni, G., Kaminski, T., Redl, C., Schüller, P. and Weinzierl, A. The DLVHEX System. Künstliche Intelligenz
2019
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Law, M., Russo, A. and Broda K. Logic-Based Learning of Answer Set Programs. In Reasoning Web. Explainable Artificial Intelligence
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2016
Cited alongside, same era.
Alviano, M., Calimeri, F., Dodaro, C., Fuscà, D., Leone, N., Perri, S., Ricca, F., Veltri, P. and Zangari, J. The ASP System DLV2. Proc. LPNMR, Springer LNCS 10377, 2017, pp. 215-221
2017
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Bertossi, L. and Salimi, B. From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back. Theory of Computing Systems
2017
Cited alongside, same era.
Eiter, T., Kaminski, T., Redl, C., Schüller, P. and Weinzierl, A. Answer Set Programming with External Source Access. Reasoning Web
2017
Cited alongside, same era.
2019
Later among the works it cites.
Rudin, C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence
2019
Later among the works it cites.
Bertossi, L. and Geerts, F. Data Quality and Explainable AI. ACM Journal of Data and Information Quality
2020
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Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F. and Schaub, T. ASP-Core-2 Input Language Format. Theory and Practice of Logic Programming
2020
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Molnar, C. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
2020
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