Augmenting Machine Learning with Argumentation
Matt Bishop, Carrie E. Gates, Karl Levitt · 2018
The information security community is haunted by the failure of an appropriate break-the-glass access control at the United States Center for Disease Control that led to an estimated additional 1.2 million deaths in North America in 2036. In this paper we review what caused the security failures in this system and argue that, by combining human intelligence with multiple technological approaches to create a system that emphasizes human approaches to guide analysis, the failures that occurred will not recur. We also leverage people and technologies to identify and fill gaps in the training data to minimize the threat of unexpected events. While we use this scenario as our running example, we note that our approach is generalizable to a broader problem space where machine learning approaches have been deployed to make decisions.