Toward Trustworthy AI: A Comprehensive Survey of XAI Methods, Software Toolkits, and Evaluation Standards

Explainable Artificial Intelligence (XAI) has emerged as a fundamental prerequisite for creating machine learning systems that are transparent, dependable, and accountable, particularly in fields where choices carry a high degree of risk. Despite the fact that numerous surveys have examined particular aspects of explainability, a more thorough approach is needed because XAI methodologies, software toolkits, evaluation protocols, and current synthesis are developing so quickly. Recent developments in three main areas are compiled in this review: (i) the methodological underpinnings of XAI, which include data level, intrinsically interpretable, and post-hoc explanation techniques; (ii) software and tool ecosystems that facilitate practical explainability through attribution based reasoning, visual analytics, surrogate modeling, and fairness or robustness assessment; and (iii) quantitative and qualitative evaluation standards that gauge explanation fidelity, stability, interpretability, human trust, and practical utility. This paper conducts a critical analysis of the advantages and disadvantages of the current approaches to XAI based on recent survey insights, benchmarking studies, and emerging evaluation frameworks. Highlighting the ongoing difficulties in implementing explainable systems in regulated, safety critical, and domain sensitive environments and fields.