While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods.Consequently, Explainability and Interpretability have emerged as important research topics in IR.Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed.We present ir_explain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework.It supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations.The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods.To facilitate adoption, ir_explain is well-integrated with widely-used toolkits such as Pyserini, PyTerrier (work in progress) and ir_datasets.Downstream applications of ir_explain include explaining the Retrieval-Augmented Generation (RAG) pipeline.The development version of the library is available on GitHub.We release the library as a pip package (https

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.

A widespread use of supervised ranking models has necessitated an investigation on how consistent their outputs align with user expectations. While a match between the user expectations and system outputs can be sought at different levels of granularity, we study this alignment for search intent transformation across a pair of queries. Specifically, we propose a consistency metric, which for a given pair of queries - one reformulated from the other with at least one term in common, measures if the change in the set of the top-retrieved documents induced by this reformulation is as per a user's expectation. Our experiments led to a number of observations, such as DRMM (an early interaction based IR model) exhibits better alignment with set-level user expectations, whereas transformer-based neural models (e.g., MonoBERT) agree more consistently with the content and rank-based expectations of overlap.

Providing high-level, intuitive explanations of the performance of IR systems is generally difficult due to their complexity, and the various low-level implementation details involved. We present I-REX, a tool built on top of Lucene, that is intended to provide a systematic view into the inner workings of retrieval models and methods (specifically query expansion). This should help researchers study, compare, understand and explain the performance of these models and methods. I-REX can be run either as a Web service accessible through a browser, or as a terminal-based tool with a shell-like interactive interface. In this article, we describe a session that illustrates how I-REX can be used to explain the observed difference in the performance of two variants of the Language Model.