Abstract Interpretation

Abstract interpretation aims to understand the internal workings and decision-making processes of complex systems, particularly machine learning models. Current research focuses on developing methods to explain model predictions, analyze feature importance, and uncover underlying algorithms, often employing techniques from dynamical systems, information theory, and category theory, and utilizing architectures like transformers, recurrent neural networks, and graph neural networks. This field is crucial for building trust in AI systems across diverse applications, from medical diagnosis and legal analysis to engineering and scientific discovery, by providing insights into model behavior and identifying potential biases or limitations.