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arXiv · 2106.06046

Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI

Mohit Kumar·Bernhard A. Moser·Lukas Fischer·Bernhard Freudenthaler
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Abstract

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI framework is introduced. A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and optimizing the tradeoffs between privacy, interpretability, and transferability aspects. A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures via maximizing a lower-bound using variational optimization. The study presents a unified information theoretic approach to study different aspects of trustworthy AI in a rigorous analytical manner. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress on individuals using heart rate variability analysis.

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Mohit Kumar, Bernhard A. Moser, Lukas Fischer, Bernhard Freudenthaler. 2022-04-12. Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI. https://arxiv.org/abs/2106.06046

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