Gender bias is a pervasive issue that continues to influence various aspects of society, including the outcomes of information retrieval (IR) systems. As these systems become increasingly integral to accessing and navigating the vast amounts of information available today, the need to understand and mitigate gender bias within them is paramount. This monograph provides a comprehensive examination of the origins, manifestations, and consequences of gender bias in IR systems, as well as the current methodologies employed to address these biases. Theoretical frameworks surrounding gender and its representation in artificial intelligence (AI) systems are explored, particularly focusing on how traditional gender binaries are perpetuated and reinforced through data and algorithmic processes. Metrics and methodologies used to identify and measure gender bias within IR systems are then analyzed, offering a detailed evaluation of existing approaches and their limitations. Subsequent sections address the sources of gender bias, including biased input queries, retrieval methods, and gold standard datasets. Various data-driven and method-level debiasing strategies are presented, including techniques for debiasing neural embeddings and algorithmic approaches aimed at reducing bias in IR system outputs. The monograph concludes with a discussion of the challenges and limitations faced by current debiasing efforts and provides insights into future research directions that could lead to more equitable and inclusive IR systems. This monograph serves as a valuable resource for researchers, practitioners, and students in the fields of information retrieval, artificial intelligence, and data science, providing the knowledge and tools needed to address gender bias and contribute to the development of fair and unbiased information systems.
Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature includes datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of text classification models relies heavily on their ability to capture intricate textual relationships and non-linear correlations, necessitating a comprehensive examination of the entire text classification pipeline. In the NLP domain, a plethora of text representation techniques and model architectures have emerged, with Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) at the forefront. These models are adept at transforming extensive textual data into meaningful vector representations encapsulating semantic information. The multidisciplinary nature of text classification, encompassing data mining, linguistics, and information retrieval, highlights the importance of collaborative research to advance the field. This work integrates traditional and contemporary text mining methodologies, fostering a holistic understanding of text classification. This monograph provides an in-depth exploration of the text classification pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of text classification models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, and future trends. Each section examines these stages, presenting technical innovations and recent findings. The work assesses various classification strategies, offering comparative analyses, examples and case studies. These contributions extend beyond a typical survey, providing a detailed and insightful exploration of the field.
Search systems are often designed to support simple look-up tasks, such as fact-finding and navigation tasks. However, people increasingly use search engines to complete tasks that require deeper learning. In recent years, the search as learning (SAL) research community has argued that search systems should also be designed to support information- seeking tasks that involve complex learning as an important outcome. This monograph aims to provide a comprehensive review of prior research in search as learning and related areas. Searching to learn can be characterized by specific learning objectives, strategies, and context. Therefore, we begin by reviewing research in education that has aimed at characterizing learning objectives, strategies, and context. Then, we review methods used in prior studies to measure learning during a search session. Here, we discuss two important recommendations for future work: (1) measuring learning retention and (2) measuring a learner's ability to transfer their new knowledge to a novel scenario. Following this, we discuss studies that have focused on understanding factors that influence learning during search and search behaviors that are predictive of learning. Next, we survey tools that have been developed to support learning during search. Searching for the purpose of learning is often a solitary activity. Research in self-regulated learning (SRL) aims to understand how people monitor and control their own learning. Therefore, we review existing models of SRL, methods to measure engagement with specific SRL processes, and tools to support effective SRL. We conclude by discussing potential areas for future research.
Mathematical information is essential for technical work, but its creation, interpretation, and search are challenging. To help address these challenges, researchers have developed multimodal search engines and mathematical question answering systems. This monograph begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions. The framework is used to organize and relate the other core topics of the monograph, including interactions between people and systems, representing math formulas in sources, and evaluation. We close by addressing some key questions and presenting directions for future work. This monograph is intended for students, instructors, and researchers interested in systems that help us find and use mathematical information.
Recent studies have shown that information retrieval systems may exhibit stereotypical gender biases in outcomes which may lead to discrimination against minority groups, such as different genders, and impact users' decisionmaking and judgements. In this tutorial, we inform the audience of studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems and different pre-trained Natural Language Processing (NLP) models. We further classify existing work on gender biases in IR systems and NLP models as being related to (1) relevance judgement datasets, (2) structure of retrievalmethods, (3) representations learnt for queries and documents, (4) and pretrained embedding models. Based on the aforementioned categories, we present a host of methods from the literature that can be leveraged tomeasure, control, or mitigate the existence of stereotypical biases within IR systems and different NLP models that are used for down-stream tasks. Besides, we introduce available datasets and collections that are widely used for studying the existence of gender biases in IR systems and NLP models, the evaluation metrics that can be used for measuring the level of bias and utility of the models, and de-biasing methods that can be leveraged to mitigate gender biases within those models.
