Faithful and Interpretable AI
Machine learning models are increasingly being applied in critical decision-making scenarios such as healthcare and finance, where transparency and interpretability are essential for establishing trust and ensuring fairness. Explainable Artificial Intelligence (XAI) methods aim to provide insights into how models make predictions. However, current techniques often involve a trade-off between accuracy and interpretability or produce explanations that are difficult to comprehend. This trade-off between accuracy and interpretability limits the effectiveness of XAI methods in practical applications. Furthermore, even when models are interpretable, their explanations may not faithfully reflect the underlying mechanisms of the machine learning model, resulting in unreliability, mistrust, and misunderstanding. To address this issue, our research aims to develop more reliable XAI methods that provide both interpretable and accurate explanations while maintaining the faithfulness of the underlying machine learning model.
Current Member: Lijie Hu
