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I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI

, , , , and . 26th International Conference on Intelligent User Interfaces, page 307-317. ACM, (April 2021)
DOI: 10.1145/3397481.3450644

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Mind the (persuasion) gap: contrasting predictions of intelligent DSS with user beliefs to improve interpretability., , and . EICS, page 2:1-2:6. ACM, (2020)reSHAPe: A Framework for Interactive Explanations in XAI Based on SHAP.. ECSCW, European Society for Socially Embedded Technologies (EUSSET), (2020)ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research., , and . NordiCHI, page 57:1-57:11. ACM, (2020)Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems., , , and . IUI Workshops, volume 2327 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Making SHAP Rap: Bridging Local and Global Insights Through Interaction and Narratives.. INTERACT (2), volume 12933 of Lecture Notes in Computer Science, page 641-651. Springer, (2021)I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI., , , , and . IUI, page 307-317. ACM, (2021)Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces., and . INTERACT (2), volume 12933 of Lecture Notes in Computer Science, page 619-640. Springer, (2021)Human-centric explanation facilities: explainable AI for the pragmatic understanding of non-expert end users.. Ludwig Maximilian University of Munich, Germany, (2021)A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI., and . ExSS-ATEC@IUI, volume 2582 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)