In order to increase productivity, capability, and data exploitation, numerous defense applications are experiencing an integration of state-of-theart machine learning and AI into their architectures. Especially for defense applications, having a human analyst in the loop is of high interest due to quality control, accountability, and complex subject matter expertise not readily automated or replicated by AI. However, many applications are suffering from a very slow transition. This may be in large part due to lack of trust, usability, and productivity, especially when adapting to unforeseen classes and changes in mission context. Interactive machine learning is a newly emerging field in which machine learning implementations are trained, optimized, evaluated, and exploited through an intuitive humancomputer interface. In this paper, we introduce interactive machine learning and explain its advantages and limitations within the context of defense applications. Furthermore, we address several of the shortcomings of interactive machine learning by discussing how cognitive feed-back may inform features, data, and results in the state of the art. We define the three techniques by which cognitive feedback may be employed: self reporting, implicit cognitive feedback, and modeled cognitive feedback. The advantages and disadvantages of each technique are discussed.DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.