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5th Workshop on Explainable User Models and Personalised Systems (ExUM).

, , , , , , and . UMAP (Adjunct Publication), page 196-198. ACM, (2023)

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Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos., , , , and . Frontiers Artif. Intell., (2021)Understanding the Role of Explanation Modality in AI-assisted Decision-making., , and . UMAP, page 223-233. ACM, (2022)Studying Topical Relevance with Evidence-based Crowdsourcing., , , , , , , and . CIKM, page 1253-1262. ACM, (2018)Machine-Crowd Annotation Workflow for Event Understanding Across Collections and Domains.. ESWC, volume 9678 of Lecture Notes in Computer Science, page 813-823. Springer, (2016)Fine-tuning machine confidence with human relevance for video discovery., and . Interactions, 29 (4): 78-82 (2022)A Checklist to Combat Cognitive Biases in Crowdsourcing., , , , and . HCOMP, page 48-59. AAAI Press, (2021)A Study of Narrative Creation by Means of Crowds and Niches., , and . HCOMP (WIP&Demo), volume 2173 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data., , , , , , , , and . ISWC (2), volume 8797 of Lecture Notes in Computer Science, page 486-504. Springer, (2014)Domain-Independent Quality Measures for Crowd Truth Disagreement., , , and . DeRiVE@ISWC, volume 1123 of CEUR Workshop Proceedings, page 2-13. CEUR-WS.org, (2013)Multi-Mode Clustering for Graph-Based Lifelog Retrieval., , , , , and . LSC@ICMR, page 36-40. ACM, (2023)