AI on Test Science Research Document Library https://research.testscience.org/keywords/ai/ Recent content in AI on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Sun, 01 Jan 2023 00:00:00 +0000 A Team-Centric Metric Framework for Testing and Evaluation of Human-Machine Teams https://research.testscience.org/post/2023-a-team-centric-metric-framework-for-testing-and-evaluation-of-human-machine-teams/ Sun, 01 Jan 2023 00:00:00 +0000 https://research.testscience.org/post/2023-a-team-centric-metric-framework-for-testing-and-evaluation-of-human-machine-teams/ We propose and present a parallelized metric framework for evaluating human-machine teams that draws upon current knowledge of human-systems interfacing and integration but is rooted in team-centric concepts. Humans and machines working together as a team involves interactions that will only increase in complexity as machines become more intelligent, capable teammates. Assessing such teams will require explicit focus on not just the human-machine interfacing but the full spectrum of interactions between and among agents. We propose and present a parallelized metric framework for evaluating human-machine teams that draws upon current knowledge of human-systems interfacing and integration but is rooted in team-centric concepts. Humans and machines working together as a team involves interactions that will only increase in complexity as machines become more intelligent, capable teammates. Assessing such teams will require explicit focus on not just the human-machine interfacing but the full spectrum of interactions between and among agents. As opposed to focusing on isolated qualities, capabilities, and performance contributions of individual team members, the proposed framework emphasizes the collective team as the fundamental unit of analysis and the interactions of the team as the key evaluation targets, with individual human and machine metrics still vital but secondary. With teammate interaction as the organizing diagnostic concept, the resulting framework arrives at a parallel assessment of the humans and machines, analyzing their individual capabilities less with respect to purely human or machine qualities and more through the prism of contributions to the team as a whole. This treatment reflects the increased machine capabilities and will allow for continued relevance as machines develop to exercise more authority and responsibility. This framework allows for identification of features specific to human-machine teaming that influence team performance and efficiency, and it provides a basis for operationalizing in specific scenarios. Potential applications of this research include test and evaluation of complex systems that rely on human-system interaction, including—though not limited to—autonomous vehicles, command and control systems, and pilot control systems.

Suggested Citation

Wilkins, Jay, David A. Sparrow, Caitlan A. Fealing, Brian D. Vickers, Kristina A. Ferguson, and Heather Wojton. “A Team-Centric Metric Framework for Testing and Evaluation of Human-Machine Teams.” Systems Engineering 27, no. 3 (May 1, 2024): 466–84. https://doi.org/10.1002/sys.21730.

Slides:

Paper:

]]> T&E Contributions to Avoiding Unintended Behaviors in Autonomous Systems https://research.testscience.org/post/2020-t-e-contributions-to-avoiding-unintended-behaviors-in-autonomous-systems/ Wed, 01 Jan 2020 00:00:00 +0000 https://research.testscience.org/post/2020-t-e-contributions-to-avoiding-unintended-behaviors-in-autonomous-systems/ To provide assurance that AI-enabled systems will behave appropriately across the range of their operating conditions without performing exhaustive testing, the DoD will need to make inferences about system decision making. However, making these inferences validly requires understanding what causally drives system decision-making, which is not possible when systems are black boxes. In this briefing, we discuss the state of the art and gaps in techniques for obtaining, verifying, validating, and accrediting (OVVA) models of system decision-making. To provide assurance that AI-enabled systems will behave appropriately across the range of their operating conditions without performing exhaustive testing, the DoD will need to make inferences about system decision making. However, making these inferences validly requires understanding what causally drives system decision-making, which is not possible when systems are black boxes. In this briefing, we discuss the state of the art and gaps in techniques for obtaining, verifying, validating, and accrediting (OVVA) models of system decision-making.

Suggested Citation

Porter, Daniel J, and Heather Wojton. T&E Contributions to Avoiding Unintended Behaviors in Autonomous Systems. Vol. IDA Document NS D-12078. Alexandria, VA: Institute for Defense Analyses, 2020.

Slides:

]]> Operational Testing of Systems with Autonomy https://research.testscience.org/post/2019-operational-testing-of-systems-with-autonomy/ Tue, 01 Jan 2019 00:00:00 +0000 https://research.testscience.org/post/2019-operational-testing-of-systems-with-autonomy/ Systems with autonomy pose unique challenges for operational test. This document provides an executive level overview of these issues and the proposed solutions and reforms. In order to be ready for the testing challenges of the next century, we will need to change the entire acquisition life cycle, starting even from initial system conceptualization. This briefing was presented to the Director, Operational Test & Evaluation along with his deputies and Chief Scientist. Systems with autonomy pose unique challenges for operational test. This document provides an executive level overview of these issues and the proposed solutions and reforms. In order to be ready for the testing challenges of the next century, we will need to change the entire acquisition life cycle, starting even from initial system conceptualization. This briefing was presented to the Director, Operational Test & Evaluation along with his deputies and Chief Scientist.

Suggested Citation

Wojton, Heather M, Daniel Porter, Yevgeniya Pinelis, Chad Bieber, Heather Wojton, Michael McAnally, and Laura Freeman. Operational Testing of Systems with Autonomy. IDA Document NS D-9266. Alexandria, VA: Institute for Defense Analyses, 2019.

Slides:

]]>