  Artificial intelligent (AI) algorithms, such as deep learning and XGboost,
are used in numerous applications including computer vision, autonomous
driving, and medical diagnostics. The robustness of these AI algorithms is of
great interest as inaccurate prediction could result in safety concerns and
limit the adoption of AI systems. In this paper, we propose a framework based
on design of experiments to systematically investigate the robustness of AI
classification algorithms. A robust classification algorithm is expected to
have high accuracy and low variability under different application scenarios.
The robustness can be affected by a wide range of factors such as the imbalance
of class labels in the training dataset, the chosen prediction algorithm, the
chosen dataset of the application, and a change of distribution in the training
and test datasets. To investigate the robustness of AI classification
algorithms, we conduct a comprehensive set of mixture experiments to collect
prediction performance results. Then statistical analyses are conducted to
understand how various factors affect the robustness of AI classification
algorithms. We summarize our findings and provide suggestions to practitioners
in AI applications.
