Perturbation-based Detection and Resolution of Cherry-picking
Perturbation-based Detection and Resolution of Cherry-picking
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发表时间：
2021
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通讯作者：
Abolfazl Asudeh;You;Wu;Cong Yu;H. V. Jagadish
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作者：
Abolfazl Asudeh;You;Wu;Cong Yu;H. V. Jagadish
在基于备选方案中的单个选项来做出结果、决策或声明的设置中，流行的做法是精挑细选数据以生成由精挑细选的数据支持的结果，但不是一般的。在本文中，我们使用扰动作为一种技术来设计一种支持措施来检测和解决不同上下文中的挑剔行为。特别是，为了展示我们建议的总体范围，我们在两个非常不同的领域进行了研究：(A)基于趋势线的政治声明和(B)线性排名。我们还讨论了基于抽样的估计作为一种有效和高效的fi逼近方法来检测和解决尺度上的挑剔行为。
In settings where an outcome, a decision, or a statement is made based on a single option among alternatives, it is popular to cherry-pick the data to generate an outcome that is supported by the cherry-picked data but not in general. In this paper, we use perturbation as a technique to design a support measure to detect, and resolve, cherry-picking across different contexts. In particular, to demonstrate the general scope of our proposal, we study cherry picking in two very different domains: (a) political statements based on trend-lines and (b) linear rankings. We also discuss sampling-based estimation as an effective and efficient approximation approach for detecting and resolving cherry-picking at scale.