Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

Computational notebooks-such as Azure, Databricks, and Jupyter-are a popular, interactive
paradigm for data scientists to author code, analyze data, and interleave visualizations, all …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

Effective collaboration in data science can leverage domain expertise from each team
member and thus improve the quality and efficiency of the work. Computational notebooks …

Ground-truth labeling is an important activity in machine learning. Many studies have
examined how crowdworkers apply labels to records in machine learning datasets …

Data scientists have embraced computational notebooks to author analysis code and
accompanying visualizations within a single document. Currently, although these media …

Computational notebooks allow data scientists to express their ideas through a combination
of code and documentation. However, data scientists often pay attention only to the code …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

Creating presentation slides is a critical but time-consuming task for data scientists. While
researchers have proposed many AI techniques to lift data scientists' burden on data …

Data science and machine learning (DS/ML) are at the heart of the recent advancements of
many Artificial Intelligence (AI) applications. There is an active research thread in AI,\autoai …