Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
Explaining a black-box using deep variational information bottleneck approach
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FAMeX creates a graph of feature associations to explain AI classification decisions and outperforms SHAP and permutation feature importance on eight benchmark datasets.
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
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A New Technique for AI Explainability using Feature Association Map
FAMeX creates a graph of feature associations to explain AI classification decisions and outperforms SHAP and permutation feature importance on eight benchmark datasets.