Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
INTERPRETABLE DATA SCIENCE FOR DECISION MAKING

INTERPRETABLE DATA SCIENCE FOR DECISION MAKING

Journal
Impact Score 5.12

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 15-07-2020
Journal Impact Score: 5.12
Journal Name: Decision Support Systems
Publisher: Decision Support Systems

Special Issue Call for Papers


DECISION SUPPORT SYSTEMS



CALL FOR PAPERS: SPECIAL ISSUE ON INTERPRETABLE DATA SCIENCE FOR DECISION MAKING



Editor

James R. Marsden, University of Connecticut, USA.



Special Issue Editors

Kristof Coussement, IESEG School of Management, France

Dries Benoit, Ghent University, Belgium



A main application of data science is to support and improve decision-making processes. Today, companies heavily invest in developing analytical and technological capabilities to enable the collection, storage and analysis of data. Their data science roadmap typically contains applications falling under descriptive, predictive and prescriptive analytics. Many businesses rely on advanced statistical and machine learning algorithms to support operational decision making across various business domains and processes, including credit risk, customer retention, human resource management, finance, fraud detection, inventory management, fleet management, and digital marketing. However, investments in improving data science capabilities are not always reflected in additional revenues or decreased costs. Decision makers are often  reluctant to rely on statistical or machine learning models if it is not immediately clear how their outcomes are obtained. Decision makers contrast the data science outcomes with their own business logic and intuitions, while underlying drivers help in personalizing their decision-making strategies. Nowadays, companies are collecting a wide variety of information resulting in both high dimensional data in terms of the number of observations and variables, and a combination of structured and unstructured like text, audio, and image data.



The prevalent focus on the data and technology has resulted in a strong emphasis on the data science practice itself, while neglecting the interpretability, explainability and actionability of the resulting outcomes to the business users. Still today, interpretability is often a key prerequisite for management to trust and deploy data science models and the lack of it could lead to diminishing practical relevance for business decision makers.



Current streams of research as reported in the literature mainly focus on investigating the beneficial impact of data preprocessing methods, new data sources like text or audio, sophisticated and scalable algorithmic developments or novel statistical evaluation metrics. Although these innovations are highly relevant in the front-end of the data science pipeline, we see a practical need and challenging opportunities for more research in bringing the outcomes of the data science pipeline closer to the needs of business decision makers. Therefore this call for submissions to a special issue on novel research on enhanced decision making through interpretable data science.



Topics of Interest



Below is an indicative list of research topics of interest:




  • innovative visualization methods of preprocessing, processing and post-processing results


  • experimental field tests and applications of interpretable machine learning methods for​decision making


  • Innovative approaches for opening black box models and/or applications


  • The development of new business-centric evaluation metrics


  • The incorporation of (human) domain knowledge in preprocessing, processing or postprocessing

    methods


  • The alignment of analytical models and operational decision processes


  • Informed feature engineering or feature learning


  • Aspects of the data collection process that affect interpretation



This call is open to all submissions that contain new, unpublished, original and fundamental work related to the topic of the call and in line with the mission of the Decision Support Systems journal.



Purely theoretical papers without a thorough empirical evaluation, survey papers, and merely incremental contributions are discouraged. All submissions will be reviewed using the journal’s screening processes and following rigorous scientific criteria whereby the novelty of the contribution to the decision support systems field will be crucial.



Submission Guidelines



Manuscripts must be submitted through the Decision Support Systems submission portal available via https://www.journals.elsevier.com/decision-support-systems. The authors should make sure to select the correct option during submission.



All manuscripts must be prepared according to the journal guidelines for authors, available at https://www.elsevier.com/journals/decision-support-systems/0167-9236/guide-for-authors.



Authors must only high-quality, original manuscript that neither has been published nor is under consideration for publication in any outlet (see the journal’s Guide for Authors)



Key Dates



Date of Submission: July 15th, 2020

First review round: October 15th, 2020

Revision: January 15th, 2021

Final decision: March, 15th, 2021



Inquiries



Informal queries regarding the expectations or the suitability of specific research topics should be directed to the special issue editors:



Kristof Coussement, IESEG School of Management, France: [email protected]

Dries Benoit, Ghent University, Belgium: [email protected]




Closed Special Issues

Publisher
Journal Details
Closing date
G2R Score
Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks

Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks

Decision Support Systems
Closing date: 31-07-2021 G2R Score: 5.12
Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks

Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks

Decision Support Systems
Closing date: 30-12-2020 G2R Score: 5.12
INTERPRETABLE DATA SCIENCE FOR DECISION MAKING

INTERPRETABLE DATA SCIENCE FOR DECISION MAKING

Decision Support Systems
Closing date: 15-07-2020 G2R Score: 5.12
Omnichannel Business: Opportunities and Challenges

Omnichannel Business: Opportunities and Challenges

Decision Support Systems
Closing date: 31-12-2016 G2R Score: 5.12