With the growing popularity of deep-learning based NLP models, comes a need for
interpretable systems. But what is interpretability, and what constitutes a high-quality …

This survey article provides a comprehensive overview of the evolving landscape of
Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As Artificial …

This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI)
methods for text classification. While existing frameworks focus on assessing XAI in areas …

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop
BlackboxNLP was dedicated to resources and techniques specifically developed for …

Analyzing textual data by means of AI models has been recognized as highly relevant in
information systems research and practice, since a vast amount of data on eCommerce …

The paper presents a novel approach to investigating mistakes in machine learning model
operations. The considered approach is the basis for BrightBox–a diagnostic technology that …

Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such
as BERTScore or MoverScore) are based on black-box language models such as BERT or …

This book presents a taxonomy framework and survey of methods relevant to explaining the
decisions and analyzing the inner workings of Natural Language Processing (NLP) models …

Outlier detection is crucial for preventing financial fraud, network intrusions, and device
failures. Users often expect systems to automatically summarize and interpret outlier …

Abstract Machine learning models built on behavioral and textual data can result in highly
accurate prediction models, but are often very difficult to interpret. Linear models require …