Looking at the FAccTs: Exploring Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendations

Music recommender systems, commonly integrated into streaming services, help listeners find music. Previous research on such systems has focused on providing the best possible recommendations for these services’ consumers, as well as on fairness for artists who release their music on streaming services. While those insights are imperative, another group of stakeholders has been omitted so far: the many other professionals working in the music industry. They, too, are (in)directly affected by music streaming services. Therefore, this work explores the perspective of music industry professionals. We present a study that addresses the role of streaming services and recommender systems in their jobs. Results indicate this role is significant. Furthermore, participants feel that music recommender systems lack transparency and are insufficiently controllable, for both customers and artists. Finally, participants desire that music streaming services take charge of increasing recommendation diversity, and variety in consumers’ listening behavior and taste

Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking

In social media effects research, the role of specific social media content is understudied, in part attributable to the fact that communication science previously lacked methods to access social media content directly. Digital trace data (DTD) can shed light on textual and audio-visual content of social media use and enable the analysis of content usage on a granular individual level that has been previously unavailable. However, because digital trace data are not specifically designed for research purposes, collection and analysis present several uncertainties. This article is a collaborative effort by scholars to provide an overview of how three methods of digital trace data collection - APIs, data donations, and tracking - can be used in studying the effects of social media content in three important topic areas of communication research: misinformation, algorithmic bias, and well-being. We address the question of how to collect raw social media content data and arrive at meaningful measures with multiple state-of-the-art data collection techniques that can be used to study the effects of social media use on different levels of detail. We conclude with a discussion of best practices for the implementation of each technique, and a comparison of their advantages and disadvantages

A Stakeholder-Centered View on Fairness in Music Recommender Systems

Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain

Viés algorítmico – um balanço provisório

This paper stands in the field of Digital Sociology and proposes to carry out a provisional bibliographic review on algorithmic bias, based on the main journals databases in English, Spanish and Portuguese. Our aim is to assess how the debate on algorithmic bias in the Humanities has developed, observing which definitions, origins, diagnostics and perspectives are presented for the phenomenon. It is observed that the majority of articles are of essayistic nature and produced in the Global North, with a low penetration of the subject in Spanish and Portuguese language literature. The expression remains broadly undefined, and is sometimes treated as synonymous with discrimination, sometimes as its cause. The main sources of bias are, according to the literature, the model development and its training data, which largely lead to recommendations for increasing transparency about the process and suggest an analytical tendency to emphasize the subjective character of algorithmic bias. There seems to be an analytical tendency in the subjective character of bias. As a perspective, we point out the importance of integrating the elements transcending the subjectivity of these actors to this analysis.Este artículo se sitúa en el campo de la Sociología Digital y propone realizar una revisión bibliográfica provisional sobre el sesgo algorítmico, basada en los principales portales de revistas en inglés, español y portugués. El objetivo es seguir el desarrollo del debate sobre el sesgo algorítmico en las Humanidades, observando las definiciones, los orígenes, los diagnósticos y las perspectivas presentadas para el fenómeno. El resultado fue que la mayoría de los artículos es de naturaleza ensayística, producidos en el Norte Global, con baja penetración del tema en la literatura en español y portugués. Hay una gran falta de definición del término, que por veces se trata como sinónimo de discriminación, por veces como su causa. Las principales fuentes de sesgo presentadas son la construcción de la herramienta y sus datos de capacitación, lo que en gran medida da lugar a sugerencias para aumentar la transparencia del proceso y sugiere una tendencia analítica a subrayar el carácter subjetivo del sesgo algorítmico. Parece haber una tendencia analítica en el carácter subjetivo del sesgo. Como perspectiva, señalamos la importancia de integrar en el análisis elementos que trasciendan la subjetividad de estos actores.Este artigo se insere no campo da Sociologia Digital e objetiva realizar um balanço bibliográfico sobre viés algorítmico (algorithmic bias) nas Humanidades, observando quais definições, causas, diagnósticos e perspectivas são apresentadas para o fenômeno. Tomando por base artigos presentes nos principais portais de periódicos nas línguas inglesa, espanhola e portuguesa, foram encontrados majoritariamente artigos de cunho ensaístico, produzidos no Norte Global, com baixa penetração da temática nas literaturas em espanhol e português. Percebe-se certa indefinição sobre o termo, ora tratado como sinônimo de discriminação, ora como sua causa. Como principais fontes de viés, foram identificadas a construção de ferramentas e os dados de treinamento. Esses fatores ressaltam a necessidade de aumentar a transparência no desenvolvimento de algoritmos; ademais, sugerem tendência analítica de enfatizar o caráter subjetivo do viés algorítmico. Esses achados salientam a importância de integrar à análise elementos que transcendem a subjetividade desses atores

Modeling Women's Elective Choices in Computing

Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. The availability of electives of interest may also make computing programs of study more meaningful to women. However, research on which elective computing topics are more appealing to women is often class or institution specific. In this study, we investigate differences in enrollment within undergraduate-level elective classes in computing to study differences between women and men. The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students (including fundamentals of programming languages and parallel and distributed computing), and which elective topics are more popular with women students (including mathematical and statistical foundations, human computer interaction and society, ethics, and professionalism). Understanding which classes appeal to different students can help departments gain insight of student choices and develop programs accordingly. Additionally, these choices can also help departments explore whether some students are less likely to choose certain classes than others, indicating potential barriers to participation in computing

Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking

In social media effects research, the role of specific social media content is understudied, in part attributable to the fact that communication science previously lacked methods to access social media content directly. Digital trace data (DTD) can shed light on textual and audio-visual content of social media use and enable the analysis of content usage on a granular individual level that has been previously unavailable. However, because digital trace data are not specifically designed for research purposes, collection and analysis present several uncertainties. This article is a collaborative effort by scholars to provide an overview of how three methods of digital trace data collection - APIs, data donations, and tracking - can be used in studying the effects of social media content in three important topic areas of communication research: misinformation, algorithmic bias, and well-being. We address the question of how to collect raw social media content data and arrive at meaningful measures with multiple state-of-the-art data collection techniques that can be used to study the effects of social media use on different levels of detail. We conclude with a discussion of best practices for the implementation of each technique, and a comparison of their advantages and disadvantages

A Meta-Study and Content Analysis of Science Fiction in Computer Science Research

Ph.D

ATEE Spring Conference 2020-2021

This book collects some of the works presented at ATEE Florence Spring Conference 2020-2021. The Conference, originally planned for May 2020, was forcefully postponed due to the dramatic insurgence of the pandemic. Despite the difficulties in this period, the Organising Committee decided anyway to keep it, although online and more than one year later, not to disperse the huge work of authors, mainly teachers, who had to face one of the hardest challenges in the last decades, in a historic period where the promotion of social justice and equal opportunities – through digital technologies and beyond – is a key factor for democratic citizenship in our societies. The Organising Committee, the University of Florence, and ATEE wish to warmly thank all the authors for their commitment and understanding, which ensured the success of the Conference. We hope this book could be, not only a witness of these pandemic times, but a hopeful sign for an equal and inclusive education in all countries