论文标题
当黑匣子算法（不）适当时：原则上的预测问题本体论
When black box algorithms are (not) appropriate: a principled prediction-problem ontology
论文作者
论文摘要
在1980年代，出现了一种新的，非常有生产力的推理方式。在本文中，我们介绍了“结果推理”一词，以参考这种推理形式。尽管结果推理已经主导了数据科学领域，但它的影响不足及其影响不足。例如，结果推理是我们推理``黑匣子''算法表现良好的主要方式。在本文中，我们分析结果推理的最常见形式（即作为“常见的任务框架”）及其局限性。我们讨论为什么大量的预测问题不适合结果推理。例如，我们发现共同的任务框架并不能为在现实世界中部署算法的部署提供基础。在建立其核心功能的基础上，我们确定了一类问题，可以将这种新形式的推理形式用于部署。我们有目的地开发一个新颖的框架，因此技术和非技术人员都可以讨论和确定其预测问题的关键特征，以及它是否适合结果推理。
In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. In this paper, we introduce the term "outcome reasoning" to refer to this form of reasoning. Though outcome reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, outcome reasoning is the primary way we reason about whether ``black box'' algorithms are performing well. In this paper we analyze outcome reasoning's most common form (i.e., as "the common task framework") and its limitations. We discuss why a large class of prediction-problems are inappropriate for outcome reasoning. As an example, we find the common task framework does not provide a foundation for the deployment of an algorithm in a real world situation. Building off of its core features, we identify a class of problems where this new form of reasoning can be used in deployment. We purposefully develop a novel framework so both technical and non-technical people can discuss and identify key features of their prediction problem and whether or not it is suitable for outcome reasoning.
