AI/ML Seminar Series: Dylan Slack (1/31/2022)

UCI AI/ML Seminar Series Dylan Slack PhD Student Department of Computer Science University of California, Irvine Exposing Shortcomings and Improving the Reliability of Machine Learning Explanations For domain experts to adopt machine learning (ML) models in high-stakes settings such as health care and law, they must understand and trust model predictions. As a result, researchers have proposed numerous ways to explain the predictions of complex ML models. However, these approaches suffer from several critical drawbacks, such as vulnerability to adversarial attacks, instability, inconsistency, and lack of guidance about accuracy and correctness. For practitioners to safely use explanations in the real world, it is vital to properly characterize the limitations of current techniques and develop improved explainability methods. This talk will describe the shortcomings of explanations and introduce current research demonstrating how they are vulnerabl