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Paul, S.K., Firdausi, T.J., Jana, S., Das, A. and Nandi, P. (2021) Counterfactual Causal Analysis on Structured Data. In: Reddy, V.S., Prasad, V.K., Wang, J. and Reddy, K., Eds., International Conference on Soft Computing and Signal Processing, Springer, 187-195.
https://doi.org/10.1007/978-981-16-7088-6_16

has been cited by the following article:

• TITLE: What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

  AUTHORS: Neelabh Kshetry, Mehmed Kantardzic

  KEYWORDS: XAI, AI, WiXAI, Causal Understanding, Counterfactuals, Counterfactual Explanation

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.6, June 28, 2024

  ABSTRACT: People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.