No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML

Alison Smith-Renner
Alison Smith-Renner
Ron Fan
Ron Fan
Melissa Birchfield
Melissa Birchfield

CHI '20: CHI Conference on Human Factors in Computing Systems Honolulu HI USA April, 2020, pp. 1-13, 2020.

被引用2|引用|浏览124|DOI:https://doi.org/10.1145/3313831.3376624
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其它链接dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

摘要

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 how explanations shape users' perceptions of ML models with or without the ability to provi...更多

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