Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

Logical analysis of data (LAD), an approach to data analysis based on Boolean functions,
combinatorics, and optimization, can be considered one of the methods of interpretable …

There have been several research works proposing new Explainable AI (XAI) methods
designed to generate model explanations having specific properties, or desiderata, such as …

Lending decisions are usually made with proprietary models that provide minimally
acceptable explanations to users. In a future world without such secrecy, what decision …

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. An individual can interpret the …

Improving customer repurchase intention constitutes a key activity for maintaining
sustainable business performance. Returning customers provide many economic and other …

In this paper, we create machine learning (ML) models to forecast home equity credit risk for
individuals using a real-world dataset, and demonstrate methods to explain the output of …

This study aims to explore the potential of Internet of Things (IoT) devices and explainable
Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM …

The paper addresses some fundamental and hotly debated issues for high-stakes event
predictions underpinning the computational approach to social sciences, especially in …