Interpretability Analysis

Interpretability analysis aims to understand how machine learning models, particularly large language models (LLMs) and deep neural networks, arrive at their predictions, thereby increasing trust and facilitating debugging. Current research focuses on developing and evaluating methods for explaining model behavior, including techniques like attention visualization, feature importance analysis (e.g., SHAP values), and counterfactual reasoning, often applied to transformer-based architectures and various deep learning models. This work is crucial for building reliable and trustworthy AI systems across diverse applications, from healthcare and industrial processes to natural language processing and computer vision, by providing insights into model biases, limitations, and decision-making processes.