Towards Faithful Agentic XAI: A Verification Method and an Open-World Benchmark for Better Model Faithfulness
arXiv:2605.27879v1 Announce Type: new Abstract: Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible through natural-language interaction, but they can also produce plausible yet unfaithful explanations. This risk arises because unreliable XAI outputs for complex models can be amplified by LLMs and mislead users. We propose Faithful Agentic XAI (FAX), a framework that improves explanation faithfulness through explicit verification. FAX decomposes draft explanations into
As AI systems, particularly those leveraging LLMs for interpretability, become more autonomous and critical, the need for trustworthiness and faithfulness in their explanations becomes paramount to prevent potential misuse or misinterpretation.
A strategic reader should care because improving the reliability and verifiability of AI explanations is crucial for broader adoption of agentic AI systems in sensitive domains and for regulatory compliance.
The focus shifts from simply generating explanations to rigorously verifying their faithfulness, thereby enhancing the trustworthiness and transparency of complex AI agents.
- · AI developers
- · Regulatory bodies
- · Enterprise AI adopters
- · Auditors and evaluators of AI
- · Developers of unfaithful XAI systems
- · Users relying on black-box AI explanations
The adoption of agentic XAI systems with built-in verification will accelerate, reducing risks associated with opaque AI.
Increased trust in AI explanations will lead to faster integration of AI agents into critical decision-making processes across industries.
Standards and regulations for AI explainability will likely incorporate faithfulness verification methods, driving a new wave of compliance requirements for AI development.
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Read at arXiv cs.AI