Understanding and evaluating your artificial intelligence (AI) system’s predictions can be challenging. AI and machine learning (ML) classifiers are subject to limitations caused by a variety of factors, including concept or data drift, edge cases, the natural uncertainty of ML training outcomes, and emerging phenomena unaccounted for in training data. These types of factors can lead to bias in a classifier’s predictions, compromising decisions made based on those predictions. The SEI has developed a new AI robustness (AIR) tool to help programs better understand and improve their AI classifier performance. In this blog post, we explain how the AIR tool works, provide an example of its use, and invite you to work with us if you want to use the AIR tool in your organization.
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