We are happy to announce a Special Issue of the journal Applied Stochastic Models in Business and Industry (ASMBI) on “Trustworthy Data Science”, dedicated to the topical areas featured in the 2024 ENBIS Spring Meeting, see https://conferences.enbis.org/e/springmeeting24
Advanced statistical and machine learning models with adaptive and intelligent methods are becoming increasingly important in applied data science. Their trustworthiness is critical for the progress and adoption of data science applications in various fields including business and industry. This ranges from methods to improve data quality, explainability, robustness and fairness to mathematical reliability guarantees.
The aim of this Special Issue is to attract high-quality, innovative, and original work on trustworthy data science that is related but not restricted to the below mentioned research fields:
• Empirical Studies on Trustworthy Data Analytics
• Explainable Artificial Intelligence in Medicine
• Fairness of Predictive Models
• Global Sensitivity Analysis
• Interpretable Machine Learning
• Methods to improve Data Quality
• Statistical Learning for Business and Industry
• Statistical Reliability and Robustness
• Trust in Intelligent Systems and Methods
• Trustworthy Design and Analysis of Computer Experiments
• Trustworthy Anomaly Detection
Submissions are not restricted to papers presented at the 2024 ENBIS Spring Meeting. All submissions will go through the standard, selective review process of ASMBI. The deadline for submissions is 30 September 2024 through the journal website: 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 “TrustworthyDS” when requested.
The Guest Editors of the special issue are Sonja Kuhnt (sonja.kuhnt@fh-dortmund.de), Sven Knoth (knoth@hsu-hh.de) and Markus Pauly (pauly@statistik.tu-dortmund.de). The guest editors 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 (nalini.ravishanker@uconn.edu).