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We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative classification and counterfactual explanations of dynamically changing graph data. We study graph classification problems ...

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we ...

We examine machine learning models in a setup where individuals have the choice to share optional personal information with a decision-making system, as seen in modern insurance pricing models. Some users consent to their data being used whereas others ...

We propose a novel and practical privacy notion called f -Membership Inference Privacy (f -MIP), which explicitly considers the capabilities of realistic adversaries under the membership inference attack threat model. Consequently, f -MIP offers ...

Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discovery methods search trained embedding spaces for interpretable concepts like ...

Predicting the future trajectories of surrounding vehicles is an important challenge in automated driving, especially in highly interactive environments such as roundabouts. Many works approach the task with behavioral cloning: A single-step prediction ...