What question did this study set out to answer?

The aim is to explore the interconnected challenges of explainability, fairness, and trust in AI systems.

February 12, 2026

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

Key Points

The aim is to explore the interconnected challenges of explainability, fairness, and trust in AI systems.
Presented novel techniques for revealing model dependencies
Aligned explanations with domain expertise
Benchmarked model classes for explanation robustness
Refined methods for measuring interpretability
Demonstrated advancements in AI transparency and fairness
Highlighted the need for governance frameworks
Outlined a future agenda for multi-aspect evaluation and context-sensitive trust

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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