From Data to Performance: Understanding and Improving Your AI Model

Modern data analytic methods and tools—including artificial intelligence (AI) and machine learning (ML) classifiers—are revolutionizing prediction capabilities and automation through their capacity to analyze and classify data. To produce such results, these methods depend on correlations. However, an overreliance on correlations can lead to prediction bias and reduced confidence in AI outputs. Drift in data and concept, evolving edge cases, and emerging phenomena can undermine the correlations that AI classifiers rely on. As the U.S. government increases its use of AI classifiers and predictors, these issues multiply (or use increase again) . Subsequently, users may grow to distrust results. To address inaccurate erroneous cor r elation s and predictions , we need new methods for ongoing testing and evaluation of AI and ML accuracy. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI) , Nicholas Testa, a senior data scientist in the SEI's Software Solutions Division (SSD) , and Crisanne Nolan, and Agile transformation engineer, also in SSD, sit down with Linda Parker Gates, Principal Investigator for this research and initiative lead for Software Acquisition Pathways at the SEI, to discuss the AI Robustness (AIR) tool , which allows users to gauge AI and ML classifier performance with data-based confidence.

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From Data to Performance: Understanding and Improving Your AI Model is an episode from Software Engineering Institute (SEI) Podcast Series by Carnegie Mellon University Software Engineering Institute.

This episode is 26:42 long.

This episode was published on Nov 10, 2025.

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