  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.
