AI Assurance using Causal Inference: Application to Public Policy
This work aims to enhance trust and fairness in AI systems for government and commercial sectors, though it appears incremental as it reviews existing techniques.
The paper addresses the lack of transparency in AI systems used for high-impact decisions, such as in public policy, by proposing causal inference methods to reveal cause-effect relationships, demonstrated through an experiment on a technology economics dataset.
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.