Elisabeth Lex

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.

Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models that do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. This survey presents a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process – so-called psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affect-aware recommender systems. For each category, the authors highlight domains in which psychological theory plays a key role. Further, they discuss selected decision-psychological phenomena that impact the interaction between a user and a recommender. They also focus on related work that investigates the evaluation of recommender systems from the user perspective and highlight user-centric evaluation frameworks, and potential research tasks for future work at the end of this survey.