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Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5703c1b8d69eb6532ca8d2b2b115ce2
http://arxiv.org/abs/2104.02712
http://arxiv.org/abs/2104.02712
Autor:
Jordan Boyd-Graber, Melissa Birchfield, Tongshuang Wu, Alison Smith-Renner, Ron Fan, Daniel S. Weld, Leah Findlater
Publikováno v:
CHI
Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates h