Explainable, Fair, and Trustworthy AI: Current Research, Regulatory Developments, and Future Directions

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Abstract
The rapid deployment of Artificial Intelligence (AI) in high-stakes domains necessitates robust approaches to transparency, fairness, and trustworthiness. Current advancements in AI performance are outpacing our understanding and ability to govern these systems. This special issue presents research addressing explainability, fairness, and trust as interconnected socio-technical challenges. Accepted papers demonstrate novel techniques for revealing hidden model dependencies, aligning explanations with domain expertise, rigorously benchmarking model classes for explanation robustness, and refining methods for measuring interpretability. We synthesize these contributions, situate them within current policy and standardization (EU AI Act; 1 NIST AI RMF; 2 , 3 ISO/IEC 23894 and 42001 4 , 5 ), and connect them to emerging evaluation science in XAI (e.g., BEExAI, 9 Saliency-Bench, 10 F-Fidelity 11 ). Finally, we outline a forward-looking agenda emphasizing multi-aspect evaluation, context-sensitive trust, and the development of governance-ready AI systems.

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