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AI Assurance using Causal Inference: Application to Public Policy

arXiv 2112.00591 ↗First posted 1 Dec 2021cs.AI cs.CY cs.GTcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 1 Dec 2021.

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References · 14

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  12. 2019Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modelingcited by 26
2 more
  1. 2018Linked Causal Variational Autoencoder for Inferring Paired Spillover Effectscited by 16
  2. 2021A Survey on AI Assurancecited by 16

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