Research Topic

Fuzzy Systems for Explainable Artificial Intelligence

About this Research Topic

Artificial intelligence and related technologies have recently been used in a wide range of areas, namely automation, control, robotics, and computing. However, as the use of intelligent systems becomes more prevalent, users are increasingly concerned with issues such as understandability and trust, since most of the current machine learning algorithms are in the form of a black box and, therefore, unexplainable.

Explainable artificial intelligence strives to make results understandable and transparent in order to explain how and why a result was obtained. The concept of explainable AI is constructed around algorithms and methods which employ artificial intelligence in such a manner that the results of the solution can be understood by humans.

Fuzzy systems are essential methods in artificial intelligence that can be applied to solve complex real-world problems. In particular, the use of fuzzy rules can facilitate the implementation of explainable AI. This is because fuzzy systems are based on approximate reasoning under uncertainty, and this is similar to how humans think and make decisions.

This Research Topic aims to highlight the latest research in the field of explainable AI and, in particular, how fuzzy systems can be used to achieve this. Papers on both theory and applications are welcome. We solicit original Research and Review articles based on survey research in the context of the following domains of applications and methodologies of Explainable AI methodologies:
• Computational behavioral science
• Learning methods for interpretable systems and models
• Applications of interpretable artificial intelligence systems
• Explainable white-box models
• Interpretability in ranking algorithms
• Theoretical approaches to explainability
• Knowledge representation and machine learning
• Self-explanatory agents and decision support systems
• Explanation agents and recommender systems
• Explainable reinforcement learning
• Explaining robot behavior
• Causal learning, causal discovery, causal reasoning, causal explanations, and causal inference
• Interpretable machine learning
• Interpretable fuzzy systems
• Fuzzy-in-the-loop artificial intelligence
• Fuzzy trees and networks
• Fuzzy control systems


Keywords: Human-centric Intelligence, Fuzzy Systems, Explainable Artificial Intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Artificial intelligence and related technologies have recently been used in a wide range of areas, namely automation, control, robotics, and computing. However, as the use of intelligent systems becomes more prevalent, users are increasingly concerned with issues such as understandability and trust, since most of the current machine learning algorithms are in the form of a black box and, therefore, unexplainable.

Explainable artificial intelligence strives to make results understandable and transparent in order to explain how and why a result was obtained. The concept of explainable AI is constructed around algorithms and methods which employ artificial intelligence in such a manner that the results of the solution can be understood by humans.

Fuzzy systems are essential methods in artificial intelligence that can be applied to solve complex real-world problems. In particular, the use of fuzzy rules can facilitate the implementation of explainable AI. This is because fuzzy systems are based on approximate reasoning under uncertainty, and this is similar to how humans think and make decisions.

This Research Topic aims to highlight the latest research in the field of explainable AI and, in particular, how fuzzy systems can be used to achieve this. Papers on both theory and applications are welcome. We solicit original Research and Review articles based on survey research in the context of the following domains of applications and methodologies of Explainable AI methodologies:
• Computational behavioral science
• Learning methods for interpretable systems and models
• Applications of interpretable artificial intelligence systems
• Explainable white-box models
• Interpretability in ranking algorithms
• Theoretical approaches to explainability
• Knowledge representation and machine learning
• Self-explanatory agents and decision support systems
• Explanation agents and recommender systems
• Explainable reinforcement learning
• Explaining robot behavior
• Causal learning, causal discovery, causal reasoning, causal explanations, and causal inference
• Interpretable machine learning
• Interpretable fuzzy systems
• Fuzzy-in-the-loop artificial intelligence
• Fuzzy trees and networks
• Fuzzy control systems


Keywords: Human-centric Intelligence, Fuzzy Systems, Explainable Artificial Intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

31 March 2021 Manuscript
31 May 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

31 March 2021 Manuscript
31 May 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..