Explainable Predictive

Explainable predictive modeling aims to create accurate predictive models while simultaneously providing transparent insights into their decision-making processes. Current research emphasizes integrating symbolic reasoning (e.g., logical rules, fuzzy logic) with neural networks to enhance interpretability, alongside the use of explainable AI (XAI) techniques like SHAP values and ICE plots to analyze feature importance. This focus on explainability is crucial for building trust in AI systems across diverse applications, from healthcare diagnostics and autonomous driving to insurance pricing and environmental monitoring, where understanding model predictions is essential for responsible deployment and effective decision-making.