Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective

no code implementations • 8 Jan 2021 • Pulei Xiong, Scott Buffett, Shahrear Iqbal, Philippe Lamontagne, Mohammad Mamun, Heather Molyneaux

In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust.

BIG-bench Machine Learning Decision Making