The 8th Workshop on Responsible Recommendation (FAccTRec 2025) was held in conjunction with the 19th ACM Conference on Recommender Systems in September, 2025 at Prague, Czech Republic, in a hybrid format.This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns.It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.For 2025, we highlight (1) the increasing importance of pre-trained models in recommendation; and (2) shifting regulatory, organizational, and political landscapes.

The 7th Workshop on Responsible Recommendation (FAccTRec 2024) was held in conjunction with the 18th ACM Conference on Recommender Systems on October, 2024 at Bari, Italy, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement. For 2024, the workshop highlights i) the possible tensions between the factors related to social responsibility, and ii) the challenges as a result of AI-related regulations in the European Union.

Information access research (and development) sometimes makes use of gender, whether to report on the demographics of participants in a user study, as inputs to personalized results or recommendations, or to make systems gender-fair, amongst other purposes. This work makes a variety of assumptions about gender, however, that are not necessarily aligned with current understandings of what gender is, how it should be encoded, and how a gender variable should be ethically used. In this work, we present a systematic review of papers on information retrieval and recommender systems that mention gender in order to document how gender is currently being used in this field. We find that most papers mentioning gender do not use an explicit gender variable, but most of those that do either focus on contextualizing results of model performance, personalizing a system based on assumptions of user gender, or auditing a model’s behavior for fairness or other privacy-related issues. Moreover, most of the papers we review rely on a binary notion of gender, even if they acknowledge that gender cannot be split into two categories. We connect these findings with scholarship on gender theory and recent work on gender in human-computer interaction and natural language processing. We conclude by making recommendations for ethical and well-grounded use of gender in building and researching information access systems.

The 6th Workshop on Responsible Recommendation (FAccTRec 2023) was held in conjunction with the 17th ACM Conference on Recommender Systems on September, 2023 at Singapore, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.

Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when the system fails to surface relevant content. Analyzing these query reformulations can inform us about both system and user behavior. In this work, we study a special category of query reformulations that involve specifying demographic group attributes, such as gender, as part of the reformulated query (e.g., ''olympic 2021 soccer results'' -> ''olympic 2021 women's soccer results"). There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness. This paper reports on an observational study of gender-specializing query reformulations---their contexts and effects---as a lens on the relationship between system results and gender, based on large-scale search log data from Bing. We find that these reformulations sometimes correct for and other times reinforce gender representation on the original result page, but typically yield better access to the ultimately-selected results. The prevalence of these reformulations---and which gender they skew towards---differ by topical context. However, we do not find evidence that either group under-representation or markedness alone adequately explains these reformulations. We hope that future research will use such reformulations as a probe for deeper investigation into gender (and other demographic) representation on the search result page.

Information access systems such as search engines and recommender systems often display results in a sorted ranked list based on their relevance. Fairness of these ranked list has received attention as an important evaluation criteria along with traditional metrics such as utility or accuracy. Fairness broadly involves both provider and consumer side fairness at both group and individual levels. Several fair ranking metrics have been proposed to measure group fairness for providers based on various “sensitive attributes”. These metrics differ in their fairness goal, assumptions, and implementations. Although there are several fair ranking metrics to measure group fairness, multiple open challenges still exist in this area to consider.

The 5th Workshop on Responsible Recommendation (FAccTRec 2022) was held in conjunction with the 16th ACM Conference on Recommender Systems on September, 2022 at Seattle, USA, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and differences is still lacking.

Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6–17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.