Simplicity and Explainability in Algorithms
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 496
Special Issue Editors
Interests: quantum computing; text processing; combinatorial algorithms; oredering and serching algorithms; graph algorithms
Special Issues, Collections and Topics in MDPI journals
Interests: cognitive science; philosophy of science; natural language processing; text processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is devoted to research on algorithmics, in all areas of computer science, but with specific attention to the more philosophical aspects related to their simplicity, elegance, and explainability, with respect not only to their design but also to their analysis.
Simplicity in design and presentation is a fundamental aspect of the success of an algorithm or computational model. Indeed, simpler algorithms, which consequently are easily understood and explained, allow a better understanding of the problem addressed, are easier to implement, and for this reason are more likely to be used by professionals and enjoy their trust.
However, the aspect of explainability, closely related to simplicity, is a multifaceted topic that in recent years has concerned the field of algorithms implemented in neural network models. In this context, explainability refers not only to the possibility of interpreting the decisions a model produces, but also to the possibility of accounting for the entire process and intention surrounding the model. In other words, we refer to the cogency of the data being used, the mathematical adequacy with respect to the task at hand, and the validity and determinability of the goal of the system itself.
Prof. Dr. Simone Faro
Dr. Arianna Pavone
Guest Editors
Manuscript Submission Information
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Keywords
- algorithmic simplicity, explainability, and elegance
- algorithm design and implementation
- neural network moderation
- simplified algorithmic analysis
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