Predicting Graceful Extensibility of Human-Machine Systems: A New Analysis Method for Evaluating Extensibility Plots to Anticipate Distributed System Performance. (December 2020)

• Record Type: Journal Article
• Authors: Morey, Dane A.; Marquisee, Jesse M.; Gifford, Ryan C.; Fitzgerald, Morgan C.; Rayo, Michael F.
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 64:Part 1(2020)
• Page Start: 313
• Page End: 318
• Publication Date: 2020-12
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181320641072
• Languages: English
• ISSNs: 1541-9312

Abstract

With all of the research and investment dedicated to artificial intelligence and other automation technologies, there is a paucity of evaluation methods for how these technologies integrate into effective joint human-machine teams. Current evaluation methods, which largely were designed to measure performance of discrete representative tasks, provide little information about how the system will perform when operating outside the bounds of the evaluation. We are exploring a method of generating Extensibility Plots, which predicts the ability of the human-machine system to respond to classes of challenges at intensities both within and outside of what was tested. In this paper we test and explore the method, using performance data collected from a healthcare setting in which a machine and nurse jointly detect signs of patient decompensation. We explore the validity and usefulness of these curves to predict the graceful extensibility of the system.