Improving Testing of Deep-Learning Systems

Artificial Intelligence (AI) and machine learning (ML) are finding applications in many domains. With their continued success, however, come significant challenges and uncertainties. This article examines testing in the realm of AI systems, focusing on one aspect of this challenge: namely, the quality of the test data (data on which an ML model is evaluated) in deep-learning systems. These systems, a subset of ML, are data-driven, and it is critical that after training these systems, they are evaluated on a test dataset that is a diverse representation of their training data distribution. Often, the test data might not have a balanced representation, leading to incorrect performance conclusions on these models.