PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models

Explainability tools currently struggle with flexibility: Existing XAI methods are often designed for specific types of neural networks or data types (like images or text), making it difficult for practitioners to apply the same explanation tool across different AI systems—a key barrier to real-world adoption of explainable AI.

PnPXAI automates the explanation selection process: The framework automatically detects what type of model and data you're working with, then recommends and optimizes the most appropriate explanation method, reducing the technical expertise required to make AI systems interpretable.

One framework can work across multiple domains: The researchers validated PnPXAI's effectiveness in diverse fields including medicine and finance, suggesting that a universal approach to AI explainability is feasible and could streamline adoption across industries.

Hyperparameter optimization improves explanation quality: The framework doesn't just select an explanation method—it also automatically tunes the method's settings to produce better explanations, addressing a gap where previous frameworks provided sub-optimal recommendations.

User validation confirms practical value: The framework's effectiveness was tested through user surveys, indicating the developers prioritized whether the explanations actually help people understand AI decisions in practice, not just whether they work in theory.