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.

Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern. Existing artificial intelligence (AI) tools for risk surveillance and diagnosis often lack adequate interpretability, fairness, and reproducibility. To address this, we proposed an Explainable AI (XAI) framework designed to answer five critical questions: why, why not, how, what if, and what else, with the goal of enhancing the explainability and transparency of AI models. We incorporated various techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), counterfactual explanations, model cards, an interactive feature manipulation interface, and the identification of similar patients to address these questions. We showcased an XAI interface prototype that adheres to this framework for predicting major postoperative complications. This initial implementation has provided valuable insights into the vast explanatory potential of our XAI framework and represents an initial step towards its clinical adoption.

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

When analysing a complex system, very often an answer to one question raises new questions. This also applies to the explanatory analysis of machine learning models. We cannot sufficiently explain a complex model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong reasoning. Surprisingly, the majority of methods developed for Explainable Artificial Intelligence (XAI) focus on a single aspect of the model behaviour. In this paper, we show the problem of model explainability as an interactive and sequential analysis of a model. We show how different XAI methods complement each other and why it is essential to juxtapose them together. The proposed process of Interactive Explanatory Model Analysis (IEMA) derives from the theoretical, algorithmic side of the model explanation and aims to embrace ideas developed in cognitive sciences. Its grammar is implemented in the modelStudio framework that adopts interactivity, customisability and automation as its main traits.