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Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs.

, , , , , , , , and . VL/HCC, page 41-48. IEEE Computer Society, (2010)

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End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression., , , , , , and . Artif. Intell., (2013)Improving Intelligent Assistants for Desktop Activities., , and . Interaction Challenges for Intelligent Assistants, volume SS-07-04 of AAAI Technical Report, page 119-121. AAAI Press, (2007)Welcome Letter., and . Proc. ACM Hum. Comput. Interact., (2018)Explainable AI: The New 42?, , , , , , , and . Machine Learning and Knowledge Extraction, page 295--303. Cham, Springer International Publishing, (2018)EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations., , , , and . CoRR, (2024)Workshop on Trust and Reliance in AI-Human Teams (TRAIT)., , , , , , and . CHI Extended Abstracts, page 116:1-116:6. ACM, (2022)End-User Feature Labeling via Locally Weighted Logistic Regression., , , , , , and . AAAI, page 1575-1578. AAAI Press, (2011)Analysis and representation of rhetorical construction of understanding in design teams' experiential learning.. University College London, UK, (2001)British Library, EThOS.From GenderMag to InclusiveMag: An Inclusive Design Meta-Method., , , , , and . VL/HCC, page 97-106. IEEE Computer Society, (2019)Toward harnessing user feedback for machine learning., , , , , , , and . IUI, page 82-91. ACM, (2007)