Integrating Causal Inference and Deep Learning in Artificial Intelligence for Transparent and Explainable Decision Making Systems

Archit Krishna Srivastava

Authors

Archit Krishna Srivastava Chennai, India Author

Keywords:

Causal Inference, Deep Learning, Explainable AI, Transparency, Decision-Making Systems, Artificial Intelligence

Abstract

The rapid advancement of artificial intelligence (AI) systems has been accompanied by increasing concerns regarding their transparency and explainability. Integrating causal inference with deep learning represents a promising avenue to address these issues, enabling the development of decision-making systems that are not only accurate but also interpretable. This paper explores the theoretical and practical benefits of merging these paradigms, discusses recent advances in the field, and proposes a unified framework for transparent AI. A combination of causal inference methods and neural networks is discussed, highlighting its potential in critical applications such as healthcare and autonomous systems. Supporting data visualization, including comparative performance analyses, underscores the transformative potential of this integration.

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