Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques / by Vladik Kreinovich

By:

Kreinovich, Vladik [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Studies in Computational Intelligence ; 1047Publisher: Cham : Springer International Publishing, 2022Publisher: Cham : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 Online-Ressource(X, 130 p. 31 illus.)ISBN:

9783031099748

Subject(s):

Additional physical formats: 9783031099731 | 9783031099755 | 9783031099762 | Erscheint auch als: 9783031099731 Druck-Ausgabe | Erscheint auch als: 9783031099755 Druck-Ausgabe | Erscheint auch als: 9783031099762 Druck-AusgabeDOI: DOI: 10.1007/978-3-031-09974-8Online resources:

Summary: Why Explainable AI? Why Fuzzy Explainable AI? What Is Fuzzy? -- Defuzzification -- Which Fuzzy Techniques? -- So How Can We Design Explainable Fuzzy AI: Ideas -- How to Make Machine Learning Itself More Explainable -- Final Self-Test.Summary: Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.PPN: PPN: 1816990213Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT

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