Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for a Novel Trustworthy AI Framework.
Year: 2021
Type: preprint
Abstract: Guidelines and principles of trustworthy AI should be adhered to in practice during the development of AI systems. This work suggests a novel information theoretic trustworthy AI framework based on the hypothesis that information theory enables taking into account the ethical AI principles during the development of machine learning and deep learning models via providing a way to study and optimize the inherent tradeoffs between trustworthy AI principles. Under the proposed framework, a unified approach to ``privacy-preserving interpretable and transferable learning'' is considered to introduce the information theoretic measures for privacy-leakage, interpretability, and transferability. A technique based on variational optimization, employing \emph{conditionally deep autoencoders}, is developed for practically calculating the defined information theoretic measures for privacy-leakage, interpretability, and transferability.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3166309912