Trustworthy AI through Causal Inference: Enhancing Interpretability of Complex Models
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The increasing complexity of artificial intelligence (AI) models has led to significant challenges in ensuring their trustworthiness, particularly in terms of interpretability, fairness, and robustness. This chapter explores the application of causal inference as a powerful framework to address these challenges. We introduce the CausalEnhanced Interpretable AI (CEIAI) framework, a novel methodology that integrates causal discovery and inference with machine learning models to enhance their transparency and fairness. Using the UCI Adult Income dataset as a case study, we demonstrate how this framework can be used to build more trustworthy AI systems. The proposed methodology combines causal graph construction, causal regularized model training, and counterfactual explanations to provide deeper insights into model behavior. Our simulation results show that the causal-enhanced model achieves a significant reduction in fairness-related disparities, such as demographic parity and equalized odds, while maintaining a high level of predictive accuracy. By leveraging causal reasoning, we can move beyond correlational patterns and develop AI systems that are not only accurate but also fair, interpretable, and aligned with human values.
References
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. “” Why should i trust you?” Explaining the predictions of any classifier”. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016, pp. 1135–1144.
- Anna Jobin, Marcello Ienca, and Effy Vayena. “The global landscape of AI ethics guidelines”. In: Nature machine intelligence 1.9 (2019), pp. 389–399.
- Judea Pearl. Causality. Cambridge university press, 2009.
- Scott M Lundberg and Su-In Lee. “A unified approach to interpreting model predictions”. In: Advances in neural information processing systems 30 (2017).
- Moritz Hardt, Eric Price, and Nati Srebro. “Equality of opportunity in supervised learning”. In: Advances in neural information processing systems 29 (2016).
- Tu Anh Hoang Nguyen et al. “Causal-Aware Generative Adversarial Networks with Reinforcement Learning”. In: arXiv preprint arXiv:2510.24046 (2025).
- Raha Moraffah et al. “Causal interpretability for machine learning-problems, methods and evaluation”. In: ACM SIGKDD Explorations Newsletter 22.1 (2020), pp. 18– 33.
- Matt J Kusner et al. “Counterfactual fairness”. In: Advances in neural information processing systems 30 (2017).
