Interpretability Tool

Interpretability tools aim to make the inner workings of complex machine learning models, particularly deep neural networks and large language models, more transparent and understandable. Current research focuses on developing methods to explain model decisions for various architectures, including convolutional neural networks (CNNs) and transformers, often employing techniques like feature attribution and dialogue-based explanations. This work is crucial for building trust in AI systems, improving model debugging and design, and facilitating responsible deployment across diverse applications, from healthcare to finance. The ultimate goal is to move beyond simply identifying model outputs to understanding the reasoning behind them.