This book provides an in-depth treatment of three important topical areas related to regulatory, ethical, and technical discussions in the context of information retrieval and recommender systems (IRRSs): (1) bias, fairness, and non-discrimination, (2) transparency and explainability, and (3) privacy and security. Sometimes referred to as trustworthiness dimensions, they are analyzed by taking an interdisciplinary perspective and incorporating views from computer science, social sciences, psychology, and law and by particularly considering the related technical challenges, societal impact, ethical considerations, and regulatory approaches.
After an introduction, the book first provides an overview of recent initiatives and already operational policies to regulate AI technology and discusses them in the context of IRRSs, focusing on regulations in Europe, the US, and China. Subsequent chapters present categories of biases, their relation to fairness and non-discrimination and ways to discover and mitigate harmful biases; major facets of transparency, with a focus on explainability (including common strategies to achieve it), traceability, and auditability; and privacy and security including technical approaches to mitigate privacy risks such as anonymization techniques and encryption methods. Eventually, the last chapter provides an outlook on the grand challenges in IRRSs, such as dealing with discrepancies between formal attempts, human perception, and regulatory frameworks for trustworthy IRRSs; understanding the capabilities and limitations of existing solutions in terms of fairness, transparency, and privacy; and adopting a multistakeholder perspective when developing solutions for fair, transparent, and privacy-preserving IRRSs.
The book targets a mostly technical readership and aims to equip it with the necessary understanding of the ethical implications of their research and development in IRRSs as well as of recent policy initiatives and regulatory approaches. While a basic knowledge of IRRSs is assumed to fully comprehend the more technical and algorithmic parts of the book, even a lay audience in terms of technical background should benefit from the book.
Markus Schedl is a full professor at Johannes Kepler University (JKU) Linz, Austria, affiliated with the Institute of Computational Perception, leading the Multimedia Mining and Search Group. In addition, he is the head of the Human-centered AI group at the Linz Institute of Technology (LIT) AI Lab. His main research interests revolve around fairness, transparency, and privacy of recommender systems and language models. Markus is a key researcher in Austria’s Cluster of Excellence project “Bilateral Artificial Intelligence” and has been the PI of numerous fundamental research projects.
Vito Walter Anelli is an assistant professor (researcher tenure track) at Politecnico di Bari, Italy. His research primarily focuses on recommender systems, knowledge representation, and user modeling. He has contributed to these fields with publications in highly recognized journals and conferences. A key area of his work involves the privacy and security of recommender systems, with particular emphasis on federated learning approaches and adversarial learning techniques. On these topics, he has delivered several tutorials and also authored a chapter of the Recommender Systems Handbook.
Elisabeth Lex is an associate professor at Graz University of Technology and principal investigator of the Recommender Systems and Social Computing Lab at the Institute of Interactive Systems and Data Science. Her research interests include recommender systems, user modeling, information retrieval, and data science, with a particular focus on psychology-informed and responsible recommender systems as well as human decision making. Elisabeth has authored numerous papers and delivered several tutorials on these topics in top venues. She also holds seminars jointly with legal scholars specialized in minority rights and non-discrimination.
