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Fairness and Transparency in Recommendation: The Users' Perspective

, , , , and . Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, page 274-279. ACM, (June 2021)How to explain recommendations that are generated from fairness prospect?.
DOI: 10.1145/3450613.3456835

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Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners' Perspective., , and . WWW, page 3648-3659. ACM, (2023)RecSys Fairness Metrics: Many to Use But Which One To Choose?, and . CoRR, (2022)Fairness and Transparency in Recommendation: The Users' Perspective., , , , and . UMAP, page 274-279. ACM, (2021)The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation., , , , , , and . FAccT, page 1652-1663. ACM, (2023)Incorporating Ethics in Computing Courses: Perspectives from Educators., , , , and . CoRR, (2022)Integrating Ethics into Introductory Programming Classes., , , , , and . SIGCSE, page 1027-1033. ACM, (2021)Incorporating Ethics in Computing Courses: Barriers, Support, and Perspectives from Educators., , , , and . SIGCSE (1), page 367-373. ACM, (2023)REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research., , , , and . FAccT, page 587-597. ACM, (2022)Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems.. RecSys, page 737-738. ACM, (2022)Recommender Systems and Algorithmic Hate., , and . RecSys, page 592-597. ACM, (2022)