1.

Record Nr.

UNINA9910595058303321

Autore

Kreinovich Vladik

Titolo

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

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022

ISBN

9783031099748

9783031099731

Edizione

[1st ed. 2022.]

Descrizione fisica

1 online resource (136 pages)

Collana

Studies in Computational Intelligence, , 1860-9503 ; ; 1047

Disciplina

006.3

Soggetti

Computational intelligence

Artificial intelligence

Engineering - Data processing

Computational Intelligence

Artificial Intelligence

Data Engineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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.

Sommario/riassunto

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.