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How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Today, the prominence of data science within organizations has given ris...
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Human-Machine Collaboration for Democratizing Data Science
Everybody wants to analyse their data, but only few posses the data scie...
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Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
The trustworthiness of data science systems in applied and real-world se...
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Computational Skills by Stealth in Secondary School Data Science
The unprecedented growth in the availability of data of all types and qu...
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How Much Automation Does a Data Scientist Want?
Data science and machine learning (DS/ML) are at the heart of the recent...
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Integrating data science ethics into an undergraduate major
We present a programmatic approach to incorporating ethics into an under...
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Seating preference analysis for hybrid workplaces
Due to the increasing nature of flexible work and the recent requirement...
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Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals from organizations of different sizes to characterize how they currently use, or intend to use, AutoML systems in their data science work. Our investigation also captures how data visualization is used in conjunction with AutoML systems. Our findings identify three usage scenarios for AutoML that resulted in a framework summarizing the level of automation desired by data workers with different levels of expertise. We surfaced the tension between speed and human oversight and found that data visualization can do a poor job balancing the two. Our findings have implications for the design and implementation of human-in-the-loop visual analytics approaches.
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