… as explainable artificial intelligence, XAI, explainable machine learning, explainable deep …
Use of SHAP in the medical domain to explain clinical decisionmaking and some of the recent …

… Explainable Artificial Intelligence (XAI) systems are born with the intention to self-explain
the reasoning behind system decisions and predictions for end users. The AI explanations, …

… , explainability issues make AI applications in clinical usages difficult. Some research has
been conducted into explainable artificial intelligence (… SHAP to explain the black-box model. …

… the impact of the explainable machine learning methods on human decision-making, we
conducted three user studies based on the three explainable methods (LIME, SHAP and CIU) …

… Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted
… Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI …

… SHAP is a model-specific framework used to explain the output of DL models by integrating
the Shapley values with DeepLIFT. Deep SHAP … using Explainable artificial intelligence in …

… In the proposed system, the authors used the SHAP method to explain and … Explainable
artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence,…

… In fact, from our study of different explainability tools (eg, LIME, SHAP, PDP), we have found
that the correlation among features is a key stumbling block to represent feature contribution …

… the BD and SHAP methods, we now transition to model explainability at an intermediate …
by the BD and SHAP values. Although the variable attribution from SHAP and BD for an …

… by SHAP. This implies that on-site monitoring can be designed to collect the selected input
variables from the SHAP … The independent variables were further analyzed using SHAP …