Abductive Reasoning, Causality, and Transparency in Explainable Artificial Intelligence: An Integrative Theoretical and Applied Analysis

Abstract

Explainable Artificial Intelligence has emerged as a central paradigm in contemporary AI research, driven by the growing societal, ethical, legal, and epistemic demands placed upon algorithmic systems. As AI increasingly mediates high-stakes decisions in domains such as healthcare, finance, manufacturing, and public governance, the opacity of complex machine learning models challenges traditional notions of understanding, trust, responsibility, and fairness. This research article develops a comprehensive theoretical and applied analysis of Explainable Artificial Intelligence by integrating philosophical foundations of explanation, abductive inference, causal reasoning, and communicative pragmatics with modern computational approaches to transparency, robustness, and fairness. Drawing strictly from established literature, the article argues that explanation in AI cannot be reduced to post-hoc visualization or feature attribution alone, but must be understood as a multi-layered socio-technical process grounded in human explanatory practices, causal models, and contextual goals. The paper synthesizes insights from cognitive science, philosophy of science, human–computer interaction, and machine learning to articulate an integrative framework for explainability that balances epistemic rigor, usability, ethical accountability, and system performance. Through extensive theoretical elaboration and descriptive analysis of existing methodologies, the article examines how abductive inference underpins both human and machine explanations, how causality and counterfactual reasoning enhance interpretability, and how transparency relates to trust calibration, fairness, robustness, and legal compliance. The discussion critically evaluates current limitations of explainable systems, including risks of false interpretability, misaligned explanations, and regulatory oversimplification. The article concludes by outlining future research directions toward causally grounded, context-aware, and socially situated explainable AI systems capable of supporting responsible and trustworthy deployment across domains.