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Mitigating Bias in Algorithmic Systems - A Fish-eye View., , , , , , , , and . ACM Comput. Surv., 55 (5): 87:1-87:37 (2023)UMAP 2020 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2020) Chairs' Welcome., , , , , and . UMAP (Adjunct Publication), page 241-243. ACM, (2020)FairUMAP 2021: The 4th Workshop on Fairness in User Modeling, Adaptation and Personalization., , , , , and . UMAP (Adjunct Publication), page 399-400. ACM, (2021)Towards Algorithmic Transparency: A Diversity Perspective., , , , , , and . CoRR, (2021)FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization., , , , , and . UMAP, page 404-405. ACM, (2020)5th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2022)., , , , , , , and . UMAP (Adjunct Publication), page 209-210. ACM, (2022)ExSS-ATEC: Explainable Smart Systems and Algorithmic Transparency in Emerging Technlologies 2020 (workshop abstract)., , , , , , , , and . ExSS-ATEC@IUI, volume 2582 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)TExSS: Transparency and Explanations in Smart Systems., , , , , , , , , and . IUI Companion, page 24-25. ACM, (2021)ExSS-ATEC: Explainable Smart Systems for Algorithmic Transparency in Emerging Technologies 2020., , , , , , , , and . IUI Companion, page 7-8. ACM, (2020)Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users' perceptions of fairness toward an algorithmic system., , and . Ethics Inf. Technol., 24 (1): 2 (2022)