Proxima: A Proxy Model-Based Approach to Influence Analysis

Machine learning (ML)-based Artificial Intelligence (AI) systems rely on training data to perform optimally, but the internal workings of how ML models learn from and use this data are often a black- box. Influence analysis provides valuable insights into the model's behavior by evaluating the effect of individual training instances on the model's predictions. However, calculating the influence of each training data can be computationally expensive. In this paper, we propose a proxy model-based approach to influence analysis called Proxima. The main idea of our approach is to use a subset of training instances to create a proxy model that is simpler than the original model, and then use the proxy model and the subset to perform influence analysis. We evaluate Proxima on eight ML models trained using real-world datasets. We compare Proxima to two state-of-the-art influence analysis tools, i.e., FastIF and Scaling-Up. Our experimental results suggest that the proposed approach can successfully perform and speed up the influence analysis process, and, in most cases, perform better than FastIF and Scaling-Up.