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- Texas A&M UniversityUS13 papers
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5 papers · 1 filter
Beyond Satisfaction: From Placebic to Actionable Explanations For Enhanced Understandability
Joe Shymanski, Jacob Brue, Sandip Sen
Explainable AI (XAI) presents useful tools to facilitate transparency and trustworthiness in machine learning systems. However, current evaluations of system explainability often r…
Not All Explanations are Created Equal: Investigating the Pitfalls of Current XAI Evaluation
Joe Shymanski, Jacob Brue, Sandip Sen
Explainable Artificial Intelligence (XAI) aims to create transparency in modern AI models by offering explanations of the models to human users. There are many ways in which resear…
Efficient Knowledge Graph Unlearning with Zeroth-order Information
Yang Xiao, Ruimeng Ye, Bohan Liu +2
Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machin…
Attack Graph Generation on HPC Clusters
Ming Li, John Hale
Attack graphs (AGs) are graphical tools to analyze the security of computer networks. By connecting the exploitation of individual vulnerabilities, AGs expose possible multi-step a…
Lessons for Cybersecurity from the American Public Health System
Adam Shostack, L. Jean Camp, Yi Ting Chua +3
The United States needs national institutions and frameworks to systematically collect cybersecurity data, measure outcomes, and coordinate responses across government and private…