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Eliciting User Preferences for Personalized Explanations for Video Summaries.

, , and . UMAP, page 98-106. ACM, (2020)

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Understanding the Role of Explanation Modality in AI-assisted Decision-making., , and . UMAP, page 223-233. ACM, (2022)Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos., , , , and . Frontiers Artif. Intell., (2021)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)Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models., , , , , , , , , and 3 other author(s). CoRR, (2023)A Survey of Crowdsourcing in Medical Image Analysis., , , , , , , , and . Hum. Comput., (2020)You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism., , , , and . HT, page 187-196. ACM, (2020)Validation Methodology for Expert-Annotated Datasets: Event Annotation Case Study., and . LDK, volume 70 of OASIcs, page 12:1-12:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)QUARE: 1st Workshop on Measuring the Quality of Explanations in Recommender Systems., , , , and . SIGIR, page 3478-3481. ACM, (2022)Crowdsourcing Inclusivity: Dealing with Diversity of Opinions, Perspectives and Ambiguity in Annotated Data., , , , , and . WWW (Companion Volume), page 1294-1295. ACM, (2019)