We are happy to announce a Special Issue of the journal Applied Stochastic Models in Business and Industry (ASMBI) on “Explainable Data Science Techniques”, dedicated to the topical areas featured in the Statistics and Data Science (SDS) Conference on 11-12 April 2024 in Palermo, Sicily, see https://meetings3.sis-statistica.org/index.php/sds2024/SDS_2024
The numerous techniques used in the field of Data Science based on the availability of large amounts of data for predictive purposes often suffer from the limitation of using algorithms that are difficult for the end user to interpret. Hence, there are several AI and ML explainable techniques that attempt to provide information on the choice of techniques, the algorithms and the processes underlying them. However, the main aspect that should be guaranteed at the end of any data analysis is the interpretability of the results, which is, of course, strongly linked to the pre-processing of the data, to the choice of the model, to the objective function optimised in the strategy of analysis, as well as to the estimation or selection of the parameters. The latest statistical research directions are moving precisely towards new developments in Statistical stochastic modelling, Statistical learning, Data Analysis methods and classification to make Data Science techniques more interpretable but also more robust and explainable. Contributions in such direction may provide useful insights into industrial applications and real-world contexts, where Data Science has become essential to support decision-making.
This special issue aims at gathering high quality contributions on innovative methods, algorithms and strategies that can improve the interpretability of ML and AI techniques in Data Science. Contributions should include demonstrations using data, with a particular focus on business and industrial applications. Articles must present new methods and/or insightful strategies of analysis based on classical techniques. Submissions are not restricted to papers presented at the SDS Conference.
All submissions will go through the standard, selective review process of ASMBI. The deadline for submissions is 15 November 2024 through the journal submission site: https://wiley.atyponrex.com/journal/ASMB
Please follow the ASMBI author submission guidelines given on the ASMBI website and click on the box of submissions to special issues, mentioning “Explainable Data Science” when prompted.
The Guest Editors of this special issue are Rosanna Verde (Rosanna.email@example.com), Paola Cerchiello (firstname.lastname@example.org), Antonella Plaia (email@example.com), and Silvia Salini (firstname.lastname@example.org). They hope that you will find it interesting to contribute to this special issue and look forward to your contributions.
For any information about the ASMBI journal, please contact its Editor-in-Chief, Nalini Ravishanker (email@example.com).