Example Based

Example-based explainable AI (XAI) aims to improve the transparency and trustworthiness of machine learning models by explaining predictions using instances from the training data. Current research focuses on developing more robust methods that address challenges like susceptibility to outliers and computational efficiency, often employing techniques like kernel methods and adversarial attacks to generate diverse and informative explanations. This area is crucial for building trust in AI systems across various domains, particularly in high-stakes applications like healthcare and education, where understanding model decisions is paramount for responsible deployment.