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A Survey of Methodsfor Explaining BlackBox Models Riccardo GuidottiAnna MonrealeSalvatore Ruggieri2018 год

A Survey of Methods for Explaining Black Box Models
review

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Аннотация: In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
Год издания: 2018
Авторы: Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi
Издательство: Association for Computing Machinery
Источник: ACM Computing Surveys
Ключевые слова: Explainable Artificial Intelligence (XAI), Adversarial Robustness in Machine Learning, Bayesian Modeling and Causal Inference
Другие ссылки: ACM Computing Surveys (PDF)
ACM Computing Surveys (HTML)
CINECA IRIS Institutial research information system (University of Pisa) (PDF)
CINECA IRIS Institutial research information system (University of Pisa) (HTML)
ricerca.sns.it (PDF)
hdl.handle.net (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)