2019/11/06 by Florian Pfisterer, Pfisterer, Florian, Janek Thomas +3
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.HC
paper · pdf · doi:10.48550/arxiv.1911.02391
4 pages
arxiv created 2019/11/06 · openalex publication_date 2019/11/06 · arxiv updated 2019/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority of the time available for a given project on data cleaning and exploratory analysis, the time available to practitioners to build actual models from data is often rather short due to time constraints for a given project. AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. In this position paper, we aim to discuss the impact of the rising popularity of such systems and how a user-centered interface for such systems could look like. More importantly, we also want to point out features that are currently missing in those systems and start to explore better usability of such systems from a data-scientists perspective.