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Special Issue: Towards robust explainable and interpretable artificial intelligence

Submission status
Closed

Over the last years, artificial intelligence (AI) models have become so complex that understanding them has raised the question about their interpretability.

The terms interpretability and explainability have been used by researchers interchangeably. These two terms sound very closely related, but according to some works one has to distinguish these two concepts. Interpretability is mostly related to the outcome of the cause-and-effect relationship given the system’s inputs. Explainability deals with the internal logic of a machine learning system. The aim is to characterize model accuracy and transparency in AI-powered decision making. It is clear that there is a need for a proper mathematical formalism that is still missing. Hence, there is a trade-off between the performance of a machine learning model and its ability to produce explainable and interpretable predictions. The study of robust systems which are also explainable and interpretable is still under way.

Explainability and interpretability have become a requirement to comply with government regulations for sensitive applications, such as in finance, public health, and transportation. In fact, this issue has received attention from the European Parliament whose General Data Protection Regulation recognizes the right to receive an explanation for algorithmic decisions. This also justifies the attention on this topic.

This Special Issue aims to collect some advancements in the field.

Topics of interest of this Special Issue include, but are not limited to:

Interpretable/explainable machine learning

Deep learning

Bio-inspired AI

Reliable AI

Interpretable fuzzy systems

Soft decision making

Statistical modelling

with all the relevant applications.

Guest Editors

Stefania Tomasiello, Institute of Computer Science, University of Tartu, Estonia

Valentina Emilia Balas, Department of Automatics and Applied Software, “Aurel Vlaicu”, University of Arad, Romania.

Feng Feng, Department of Applied Mathematics, Xi’an University of Posts and Telecommunications, China

Yichuan Zhao, Department of Mathematics and Statistics, Georgia State University, United States

Manuscript Submission: March 7, 2023

Notification of Acceptance: April 30, 2023

Final Manuscript Due: June 30, 2023

Tentative Publication Date: August 1, 2023

Editors

  • Stefania Tomasiello Stefania Tomasiello , 

    Stefania Tomasiello

    Institute of Computer Science, University of Tartu, Estonia

  • Valentina Emilia Balas Valentina Emilia Balas , 

    Valentina Emilia Balas

    Department of Automatics and Applied Software, “Aurel Vlaicu”, University of Arad, Romania.

  • Feng Feng Feng Feng  &

    Feng Feng

    Department of Applied Mathematics, Xi’an University of Posts and Telecommunications, China

  • Yichuan Zhao Yichuan Zhao

    Yichuan Zhao

    Department of Mathematics and Statistics, Georgia State University, United States

Articles (13 in this collection)