使用因果推理的人工智能保证:在公共政策中的应用(CS)

开发和实施基于人工智能的解决方案有助于州和联邦政府机构、研究机构和商业公司加强决策过程，自动化连锁操作，并减少自然和人力资源的消耗。与此同时，在实践中使用的大多数人工智能方法只能表现为“黑箱”，缺乏透明度。这最终可能导致意想不到的结果，并破坏对此类系统的信任。因此，至关重要的不仅是开发有效和健壮的人工智能系统，而且要确保它们的内部流程是可解释的和公平的。在本章中，我们的目标是通过美国经济技术部门的例子，介绍为具有高影响决策的AI系统设计保证方法的主题。我们通过提供对技术经济学数据集的因果实验，解释了这些领域如何从揭示数据集中关键指标之间的因果关系中受益。本文回顾了几种因果推理方法和人工智能保证技术，并演示了将数据转换为图结构数据集的方法。

原文题目：AI Assurance using Causal Inference: Application to Public Policy

原文：Developing and implementing AI-based solutions help state and federal government agencies, research institutions, and commercial companies enhance decision-making processes, automate chain operations, and reduce the consumption of natural and human resources. At the same time, most AI approaches used in practice can only be represented as "black boxes" and suffer from the lack of transparency. This can eventually lead to unexpected outcomes and undermine trust in such systems. Therefore, it is crucial not only to develop effective and robust AI systems, but to make sure their internal processes are explainable and fair. Our goal in this chapter is to introduce the topic of designing assurance methods for AI systems with high-impact decisions using the example of the technology sector of the US economy. We explain how these fields would benefit from revealing cause-effect relationships between key metrics in the dataset by providing the causal experiment on technology economics dataset. Several causal inference approaches and AI assurance techniques are reviewed and the transformation of the data into a graph-structured dataset is demonstrated.