Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “(Doshi-Velez and Kim, 2017; J. Doyle, Radzicki, and Trees, 2008; Freitas, 2014; Groce et al., 2014; Kulesza et al., 2011; Kulesza, Stumpf, Burnett, and Kwan, 2012; T. Miller, 2017; T. Miller et al., 2017; Pacer, Williams, Chen, Lombrozo, and Griffiths, 2013; Sauro and Dumas, 2009; Shinsel et al.,…”
The Role of Explanations in Assessing and Correcting Personalized Intelligent Agents (2012, methodology): “Further, our coverage technique helped participants assess more than twice as many predictions as the control group [3].”
You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014, methodology): “This paper encapsulates our previous work, which introduced a user interface (WYSIWYT/ML) [32] to support end users testing classifiers, and makes the following additional contributions: Formally frames a test selection problem.”
Too much, too little, or just right? Ways explanations impact end users' mental models (2013, background): “Although research has shown that certainty is a useful intelligibility type to users assessing an intelligent agent’s reliability [10], other researchers have found that certainty does not help users’ perceived understanding of how a recommender operates [4].”