Explainability and causality for robust, fair, and trustworthy artificial reasoning

Atul Rawal; Adrienne Raglin; Brian M. Sadler; Danda B. Rawat

doi:10.1117/12.2666085

20 June 2023 Explainability and causality for robust, fair, and trustworthy artificial reasoning

Atul Rawal, Adrienne Raglin, Brian M. Sadler, Danda B. Rawat

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381R (2023) https://doi.org/10.1117/12.2666085
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Artificial intelligence (AI) and machine learning (ML) systems are required to be fair and trustworthy. They must be capable of bias detection and mitigation to achieve robustness. To this end, a plethora of research fields have seen growth in research related to making AI/ML systems more trustworthy. Causal learning and Explainable AI (XAI) are two such fields that have been used extensively in the past few years to achieve explainability and fairness. However, they have been used as separate methodologies, not together. This paper provides a new perspective in using causal learning and XAI together to create a more robust and trustworthy system. Having causality and explainability together in the same model presents an extra layer of robustness, that is not achieved by using either of them individually. We present a use case for combining causality via causal discovery, and explainability via feature relevance. Using causal discovery, the generated causal graphs are compared to the feature relevance plots from the ML model. Directed causal graphs can display the features that are causally relevant for the predictions, and these causally relevant features can be directly compared to the features listed from correlation-based explanations from XAI.

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Atul Rawal, Adrienne Raglin, Brian M. Sadler, and Danda B. Rawat "Explainability and causality for robust, fair, and trustworthy artificial reasoning", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381R (20 June 2023); https://doi.org/10.1117/12.2666085

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KEYWORDS

Machine learning

Artificial intelligence

Systems modeling

Evolutionary algorithms

Algorithm development

Library classification systems

Data modeling

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