XAI Based

Explainable AI (XAI) focuses on making the decision-making processes of complex AI models, particularly deep neural networks, more transparent and understandable. Current research emphasizes developing methods to generate both local (instance-specific) and global (model-wide) explanations, often using techniques like integrated gradients, concept-based explanations, and counterfactual analysis, and exploring how these explanations can improve model performance, mitigate biases like anchoring bias, and enhance trust in AI systems. This work is crucial for building reliable and trustworthy AI systems across diverse applications, from medical diagnosis to autonomous driving, by bridging the gap between human understanding and complex AI decision-making.