Explainability and causality for robust, fair, and trustworthy artificial reasoning
Atul Rawal, Adrienne J. Raglin, Brian M. Sadler, Danda B. Rawat · 2023
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.