explainability

Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their …

Identifying the drivers of change in time-sensitive domains like healthcare is critical for reliable decision-making, yet explanations …

Recent advancements in graph neural networks (GNNs) have significantly enhanced the performance of AI systems in tasks such as …

Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted …

I gave a talk on my paper on Dynamic Graph Counterfactuality accepted as oral presentation at KDD'24.

We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative …

The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led …

I gave a talk on my paper on Generative Graph Counterfactuality accepted as oral presentation at AAAI'24.

Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI …