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  they trust the outputs. In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems. Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understandingand trust in the AutoAI system.
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.
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