Towards robust explainable and interpretable artificial intelligence

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


Tentative schedule

Manuscript Submission

December 31, 2021

Notification of Acceptance

February 28, 2022

Final Manuscript Due

April 30, 2022

Tentative Publication Date

September 1, 2022