AI Assurance using Causal Inference: Application to Public Policy

TL;DR

This paper explores how causal inference can enhance AI assurance by improving transparency and fairness, demonstrated through applications in US technology sector decision-making.

Contribution

It introduces a framework for applying causal inference techniques to AI assurance, emphasizing explainability and trustworthiness in high-impact decisions.

Findings

Causal relationships can be effectively revealed in AI datasets.

Transforming data into graph structures aids causal analysis.

Causal inference improves AI system transparency.

Abstract

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.