Publikation

Springer Book Chapter: "Interpretable Artificial Intelligence: A Perspective of Granular Computing"

Nijat Mehdiyev, Peter Fettke

In: Witold Pedrycz, Shyi-Ming Chen (Hrsg.). Interpretable Artificial Intelligence: A Perspective of Granular Computing. ISBN 9783030649487 Springer 2/2021.

Abstrakt

In the recent years, Artificial Intelligence (AI) has emerged as an important, timely, and far reaching research discipline with a plethora of advanced applications. With the rapid progress of AI concepts and methods, there is also a recent trend to augment the paradigm by bringing aspects of explainability. With the ever growing complexity of AI constructs their relationships with data analytics (and inherent danger of cyberattacks and adversarial data) and the omnipresence of demanding applications in various critical domains, there is a growing need to associate the results with sound explanations All of these factors have given rise to the most recent direction of Explainable AI (XAI). Augmenting AI with the facets of human centricity becomes indispensable. It is desirable that the models of AI are transparent so that the results being produced have to be easily interpretable and explainable. There have been a number of studies emphasizing that opaque constructs of artificial neural networks including deep learning and ways to bringing the aspect of transparency To make the results interpretable and furnish the required facet of explainability, one may argue that the findings have to be delivered at a certain level of abstraction (general perspective) –as such information granularity and information granules play here a pivotal role. Likewise, the explanation mechanisms could be inherently associated with the logic fabric of the constructs, which facilitate the realization of interpretation and explanation processes. The objective of the volume is to provide the readership with a comprehensive and up-to-date treatise in the area of XAI covering a spectrum of methodological and algorithmic issues, discussing implementations and case studies, identifying the best design practices, assessing implementation models and practices of XAI in industry, health care, administration, and business.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence