2 papers
cs.CR2024
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju
Predictive machine learning models are becoming increasingly deployed in high-stakes contexts involving sensitive personal data; in these contexts, there is a trade-off between mod…
cs.LG2024
Towards Non-Adversarial Algorithmic Recourse
Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj +1
The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate th…