  The quality of Artificial Intelligence (AI) algorithms is of significant
importance for confidently adopting algorithms in various applications such as
cybersecurity, healthcare, and autonomous driving. This work presents a
principled framework of using a design-of-experimental approach to
systematically evaluate the quality of AI algorithms, named as Do-AIQ.
Specifically, we focus on investigating the quality of the AI mislabel data
algorithm against data poisoning. The performance of AI algorithms is affected
by hyperparameters in the algorithm and data quality, particularly, data
mislabeling, class imbalance, and data types. To evaluate the quality of the AI
algorithms and obtain a trustworthy assessment on the quality of the
algorithms, we establish a design-of-experiment framework to construct an
efficient space-filling design in a high-dimensional constraint space and
develop an effective surrogate model using additive Gaussian process to enable
the emulation of the quality of AI algorithms. Both theoretical and numerical
studies are conducted to justify the merits of the proposed framework. The
proposed framework can set an exemplar for AI algorithm to enhance the AI
assurance of robustness, reproducibility, and transparency.
