Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported cooperative work, data base management, decision theory, economics, linguistics, management science, mathematical modeling, operations management psychology, user interface management systems, and others. The common thread of articles published in the journal will be their relevance to theoretical, technical DSS issues.

Authors planning to submit papers to the journal should ensure that their work is relevant to the topics normally considered to be part of the field of decision support systems.

The Journal's research papers tend to fall into the following six topic departments:

1. DSS Foundations e.g. DSS principles, concepts, and theories; frameworks, formal languages, and methods for DSS research; tutorials about the nature of DSS; assessments of the DSS field.

2. DSS Development-Functionality e.g. methods, tools, and techniques for developing the underlying functional aspects of a DSS; solver/model management; data management in DSSs; rule management and AI in DSSs; coordinating a DSS's functionality within its user interface.

3. DSS Development-Interfaces e.g. methods, tools, and techniques for developing the overt user interface of a DSS; managing linguistic, presentation, and user knowledge in a DSS; DSS help facilities; coordinating a DSS's interface events with its functionality events.

4. DSS Impacts and Evaluation e.g. DSS economics; DSS measurement; DSS impacts on individual users, multiparticipant users, organizations, and societies; evaluating/justifying DSSs.

5. DSS Reference Studies e.g. reference discipline tutorials for DSS researchers; emerging technologies relevant to DSS characteristics or DSS development; related studies on such topics as communication support systems, computer supported cooperative work, negotiation support systems, research support systems, task support systems.

6. DSS Experiences, Management, and Education e.g. experiences in developing or operating DSSs; systems solutions to specific decision support needs; approaches to managing DSSs; DSS instruction/training approaches.

Special Issue on Explainable AI for Enhanced Decision Making
Submission Date: 2023-06-15

Artificial Intelligence (AI) defined as the development of computer systems that are able to perform tasks normally requiring human intelligence by understanding, processing and analyzing large amounts of data has been a prevalent domain for several decades now. An increasing number of businesses rely on AI to achieve outcomes that operationally and/or strategically support (human) decision making in different domains. At present, AI based machine learning (ML) has become widely popular as a subfield of AI, both in industry as in academia. ML has been widely used to enhance decision making including predicting organ transplantation risk (Topuz et al., 2018), forecasting remaining useful life of machinery (Kraus et al., 2020), student dropout prediction (Coussement et al., 2020), money laundering (Fu et al., 2021), money laundering detection (Vandervorst et al., 2022) amongst others. In the early days, AI attempts to imitate human decision-making rules were only partially successful, as humans often could not accurately describe the decision-making rules, they use to solve problems (Fügener et al., 2022). With the development of advanced AI, exciting progress has been made in algorithmic development to support decision making in various fields including finance, economics, marketing, human resource management, tourism, computer science, biological science, medical science, and others (Liu et al., 2022).

Recently, advances have heavily focused on boosting the predictive accuracy of AI methods, with deep learning (DL) methods being a prevalent example. The stringent focus on improved prediction performance often comes at the expense of missed explainability, which leads to decision makers’ distrust and even rejection of AI systems (Shin, 2021). Explainable AI describes the process that allows one to understand how an AI system decides, predicts, and performs its operations. Therefore, explainable AI reveals the strengths and weaknesses of the decision-making strategy and explains the rationale of the decision support system (Rai, 2020). Numerous scholars confirm that explainable AI is the key to developing and deploying AI in industries such as retail, banking and financial services, manufacturing, and supply chain/logistics (Kim et al., 2020; Shin, 2021; Zhdanov et al., 2022). In addition, explainable AI has also received attention from governments due to its ability to improve the efficiency and effectiveness of governments’ functionalities and decision supports (Phillips-Wren et al., 2021).

In fact, in many cases, understanding why a model makes certain decisions and predictions is as important as its accuracy. Because model explainability helps managers to better understand models’ parameters and apply them more confidently, allowing managers to communicate the analytical rationale more convincingly for their decisions to stakeholders (Wang et al., 2022). Among others, exploring the applications of AI explainability and precise understandability in decision making is one of the main contributions of this special issue.

Therefore, this special issue proposal on “Explainable AI for Enhanced Decision Making” deals with the following topics as an illustrative but not restrictive list:

Explainability and interpretability in AI decision support systems
Use explainable AI for corporate investment decisions
Explainable AI in banking, insurance, and micro enterprises
Explainable AI in healthcare, transportation, and education
Causality of AI models
Property risk assessment using explainable AI
Make enhanced business decisions using explainable AI
Use explainable AI to make predictions for IT industry decisions
Explainable AI, big data, and decision support systems
Explainable AI, applications and services
Explainable methods for deep learning architectures
Decision model visualization
Evaluate decision making metrics and processes
Measuring explainability in decision support systems