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Enhancing interpretability and accuracy of AI models in . . . To address these challenges, future research should focus on developing hybrid models that balance accuracy and interpretability Additionally, incorporating uncertainty quantification methods can improve the reliability of AI models in healthcare
Explainability and uncertainty: Two sides of the same coin . . . This position paper proposes the integration of Uncertainty Quantification (UQ) with XAI methods to improve model reliability and trustworthiness in healthcare applications We examine state-of-the-art XAI and UQ techniques, discuss implementation challenges, and suggest solutions to combine UQ with XAI methods
EXPLAINABLE AI (XAI): IMPROVING TRANSPARENCY IN MACHINE . . . This paper explores key methodologies in XAI, including feature attribution, surrogate models, and visualization techniques, emphasizing their role in bridging the gap between complex ML models
ML and AI Model Explainability and Interpretability LIME and SHAP are powerful tools that improve the explainability of machine learning and AI models They make complex or black-box models more transparent LIME specializes in providing local-level insights into a model’s decision-making process SHAP offers a broader view, explaining feature contributions at both global and local levels
Explainable AI (XAI): Methods and Techniques to Make Deep . . . This paper offers a thorough analysis of the strategies and tactics used to improve the interpretability of deep learning models, including hybrid approaches, post-hoc explanations, and model-specific strategies
Interpretability and Explainability of Machine Learning . . . If accomplished, transparency issues, including interpretability and explainability would contribute to a better understanding of how a model works, providing a justification for its outcomes, increasing confidence in the use of such models, and effectively assisting clinicians in decision-making
Interpretable and explainable machine learning: A methods . . . Interpretability and explainability are crucial for machine learning (ML) and statistical applications in medicine, economics, law, and natural sciences and form an essential principle for ML model design and development