This chapter discusses recent regulatory and ethical aspects of transparency, clarifies the relevant terminology, and highlights the benefits, challenges, and barriers to transparency in IRRSs. It then discusses techniques for enhancing transparency in IRRSs, outlines how transparency can be evaluated and achieved via documentation, and presents how algorithmic auditing helps improve the transparency of IRRSs. The chapter concludes with highlighting open challenges and further related work.

This chapter investigates regulatory activities and policies in the European Union, the USA, and—considering its highly different cultural and political background—China, where regulation of AI and IRRSs has quite different aims than in the Western world.

This chapter outlines potential privacy and security risks inherent in IRRSs. It explores how personal data and models can be protected and discusses relevant regulations and corresponding technical solutions. The chapter closes by discussing open challenges related to privacy and security in IRRSs and points to additional related work.

This chapter reviews existing regulations on discrimination, provides a multifaceted categorization of biases and fairness criteria, discusses techniques to measure popularity and demographic biases, presents methods to mitigate harmful biases, and concludes with a reflection on open challenges.

This chapter briefly synthesizes the key themes discussed throughout this work and outlines a roadmap for future research directions in this critical field.

This chapter introduces the main objectives and target audience of the book, provides a brief introduction to the fundamentals of IRRSs, raises awareness of the various stakeholders involved in IRRSs, and delineates some use cases to illustrate concepts and solutions discussed throughout the book.

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. (shrink)