Automation Accuracy Is Good, but High Controllability May Be Better
Automation Accuracy Is Good, but High Controllability May Be Better
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自动化精度好，但可控性高可能更好
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Daniel Vogel
中科院分区：
文献类型：
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作者：
Quentin Roy;Futian Zhang;Daniel Vogel
当使用某种形式的人工智能自动化任务时，结果中的一些不准确性几乎是不可避免的。在许多情况下，用户必须决定是再次尝试自动化方法，还是使用可用的用户界面自行修复。我们认为，这一决定是由感知自动化的准确性和任务的“可控性”的程度（如何容易和在何种程度上自动化的结果可以手动修改）的影响。准确性和可控性之间的关系是在一个750人的众包实验中使用一个受控的，游戏化的任务进行研究。具有高可控性，自我报告的满意度保持不变，即使在非常低的准确性条件下，总体而言，一个强烈的偏好，观察到使用手动控制，而不是自动化，尽管慢得多的性能，无论非常差的可控性。
When automating tasks using some form of artificial intelligence, some inaccuracy in the result is virtually unavoidable. In many cases, the user must decide whether to try the automated method again, or fix it themselves using the available user interface. We argue this decision is influenced by both perceived automation accuracy and degree of task "controllability" (how easily and to what extent an automated result can be manually modified). This relationship between accuracy and controllability is investigated in a 750-participant crowdsourced experiment using a controlled, gamified task. With high controllability, self-reported satisfaction remained constant even under very low accuracy conditions, and overall, a strong preference was observed for using manual control rather than automation, despite much slower performance and regardless of very poor controllability.