Recently, post hoc explanation methods have emerged to enhance model transparency by attributing model outputs to input features. However, these methods face challenges due to their specificity to certain neural network architectures and data modalities. Existing explainable artificial intelligence (XAI) frameworks have attempted to address these challenges but suffer from several limitations. These include limited flexibility to diverse model architectures and data modalities due to hard-coded implementations, a restricted number of supported XAI methods because of the requirements for layer-specific operations of attribution methods, and sub-optimal recommendations of explanations due to the lack of evaluation and optimization phases. Consequently, these limitations impede the adoption of XAI technology in real-world applications, making it difficult for practitioners to select the optimal explanation method for their domain. To address these limitations, we introduce \textbf{PnPXAI}, a universal XAI framework that supports diverse data modalities and neural network models in a Plug-and-Play (PnP) manner. PnPXAI automatically detects model architectures, recommends applicable explanation methods, and optimizes hyperparameters for optimal explanations. We validate the framework's effectiveness through user surveys and showcase its versatility across various domains, including medicine and finance.

本研究解决了现有可解释人工智能（XAI）框架在处理不同神经网络架构和数据模式时的局限性问题。提出的PnPXAI框架具备自动检测模型架构、推荐适用解释方法及优化超参数的能力，显著提高了解释的灵活性和有效性。实验结果表明，PnPXAI能够在医疗和金融等多个领域中广泛应用，推动XAI技术的实际采用。

PnPXAI：一种提供跨多种模式和模型的自动解释的通用可解释人工智能框架