  Artificial Intelligence (AI) can now automate the algorithm selection,
feature engineering, and hyperparameter tuning steps in a machine learning
workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve
data scientists from the tedious manual work. However, today's AutoAI systems
often present only limited to no information about the process of how they
select and generate model results. Thus, users often do not understand the
process, neither do they trust the outputs. In this short paper, we provide a
first user evaluation by 10 data scientists of an experimental system,
AutoAIViz, that aims to visualize AutoAI's model generation process. We find
that the proposed system helps users to complete the data science tasks, and
increases their understanding, toward the goal of increasing trust in the
AutoAI system.
