  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.
