Interpretable Statistical Machine for Decision Making: Modeling, Learning and Application Perspectives

Dear Colleagues,

Machine learning (ML) algorithms are powerful tools used in making decisions and predictions in real-world complex systems, including medical diagnoses, the spread of infectious diseases, financial modelling and insurance pricing. However, in most cases, these algorithms do not provided explanations for how they reach the results and have a barrier preventing the achievement of a broader adaptation of machine learning or, more generally, artificial intelligence (AI) technologies in decision-making problems. Most ML or deep learning (DL) algorithms in AI are complex, and explaining and interpreting their results is not straightforward. The difficulties with explaining and interpreting AI results have led to research on the interpretability of AI. On the other hand, interpretable decision making is essential, particularly for policymakers or government regulators, as transparency of how the decision is determined is part of the decision-making process. Such a need has caused explainable AI and interpretable machine learning to become prevalent in many fields, including insurance, finance and engineering. For example, this type of research is crucial for the decision making of auto insurance rate regulation, and is currently a very active research area in actuarial science.

This Special Issue aims to collect outstanding research papers on building statistical or computational models that can provide sufficient interpretability for decision-making problems. The application domains may include computational intelligence, engineering, business and economics.

The methodology topics include, but are not limited to, the following:

• Sparse statistical methods;
• Interpretable statistical models;
• Feature extraction and dimension reduction;
• Explainable artificial neural networks;
• Model agnostics methods;
• Variable importance measures;
• Machine learning for decision making;
• Multiple criteria decision making.

Both theoretical development and applied work addressing the interpretability of models for decision making in a broader sense are welcome.

Dr. Xiaojun Ban
Dr. Shengkun Xie
Guest Editors

Manuscript Submission Information

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• interpretable machine learning
• statistical learning
• decision making under uncertainty
• high-dimensional data analysis
• feature extraction
• explainable machine learning
• multiple criteria decision making