Authors: Dr. Jonathan Reed, Dr. Emily Carter, Michael Thompson, Dr. Sophia Reynolds, Andrew Richard
Abstract: The increasing deployment of machine learning (ML) systems in high-stakes domains such as healthcare, finance, criminal justice, and autonomous systems has significantly intensified concerns about transparency, accountability, reliability, and societal trust. While modern ML models particularly deep neural networks have demonstrated superior predictive performance, their complex, non-linear architectures often render them opaque, leading to criticism that they function as “black boxes” whose internal reasoning is difficult for humans to interpret, audit, or validate. This lack of interpretability poses risks in safety-critical and regulated environments, where stakeholders require clear, understandable justifications for automated decisions. In response to these challenges, Explainable Artificial Intelligence (XAI) has emerged as a crucial and rapidly evolving research area aimed at designing methods that make AI systems more interpretable, transparent, and aligned with human values, ethical principles, and legal requirements. This article presents a systematic review of Explainable AI techniques developed between 2000 and 2021, focusing on their role in enabling trustworthy machine learning systems by structuring the landscape of XAI into intrinsic (interpretable-by-design) and post-hoc (after-the-fact explanation) approaches, examining representative and widely adopted techniques such as LIME, SHAP, and Integrated Gradients, and critically discussing the methodological and practical challenges in evaluating explanation quality. Furthermore, the review analyzes how XAI intersects with broader principles of trustworthy AI including fairness, accountability, transparency, robustness, and human oversight while identifying key research gaps and outlining future directions for developing more reliable, human-centered, and socially responsible AI systems.
DOI: https://doi.org/10.5281/zenodo.19106811
