Making Machine Learning Trustworthy

Abstract

Machine learning (ML) has advanced dramatically during the past decade and continues to achieve impressive human-level performance on nontrivial tasks in image, speech, and text recognition. It is increasingly powering many high-stake application domains such as autonomous vehicles, self–mission-fulfilling drones, intrusion detection, medical image classification, and financial predictions. However, ML must make several advances before it can be deployed with confidence in domains where it directly affects humans at training and operation, in which cases security, privacy, safety, and fairness are all essential considerations.