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Principles for Evaluation of AI/ML Model Performance and Robustness
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The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to be brittle and nonrobust. In a complex and ever-changing national security environment, it is vital that the DoD establish a sound and methodical process to evaluate the performance and robustness of AI/ML models before these new capabilities are deployed to the field. This paper reviews the AI/ML development process, highlights common best practices for AI/ML model evaluation, and makes recommendations to DoD evaluators to ensure the deployment of robust AI/ML capabilities for national security needs.
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Cited by 1 Pith paper
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Towards Modeling Data Quality and Machine Learning Model Performance
The paper repackages signal-to-noise ratio as a data-quality metric, DDR, and uses controlled synthetic noise to draw accuracy-DDR curves and define a trustworthiness portfolio.
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