With the ever-increasing popularity and applications of graph neural networks, several
proposals have been made to explain and understand the decisions of a graph neural …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on
fostering responsible and trustworthy deployment of machine learning systems in the context …

It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

Recent work has shown that incorporating explanations into the output generated by large
language models (LLMs) can significantly enhance performance on a broad spectrum of …

This paper proposes a novel approach towards better interpretability of a trained text-based
ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text …

Recent work has shown that inducing a large language model (LLM) to generate
explanations prior to outputting an answer is an effective strategy to improve performance on …

Understanding why a model makes certain predictions is crucial when adapting it for real
world decision making. LIME is a popular model-agnostic feature attribution method for the …

A Trustworthy View on Explainable Artificial Intelligence Method Evaluation Page 1 COVER
FEATURE SOFTWARE ENGINEERING FOR RESPONSIBLE AI 50 COMPUTER PUBLISHED …

Feature attributions are a commonly used explanation type, when we want to posthoc
explain the prediction of a trained model. Yet, they are not very well explored in IR …