lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Data scientists are inefficient and error-prone when handling complex data cleaning tasks.Direction: General AI Systems and Interaction Design
Scientific Anomaly Detection and Causal Analysis Support
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7 items
lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Marine scientists lack coherent, easy-to-use software tools, affecting collaboration and efficiency.lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Data scientists struggle to efficiently perform nonlinear exploration and rollback on complex tasks.lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Data scientists lack ethics training tools for real work scenarios, causing ethical discussions to be disconnected from practice.lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Scientists struggle to analyze Martian geochemical data with existing anomaly detection tools.IUI '23Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Data scientists struggle to rapidly acquire domain knowledge, lowering machine learning development efficiency.lightbulbPractical problemScientific Anomaly Detection and Causal Analysis Support
Data scientists find existing tools complex and inefficient for task handling.Related papers
CHI 2025
Dango: A Mixed-Initiative Data Wrangling System using Large Language Model
Wei-Hao Chen, Weixi Tong, Amanda Case
CHI 2025
Understanding Marine Scientist Software Tool Use
Matthew Lakier, Andrew Irwin, Daniel Vogel
CHI 2025
Enhancing Computational Notebooks with Code+Data Space Versioning
Hanxi Fang, Supawit Chockchowwat, Hari Sundaram
CHI 2024
Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams
Vanessa Aisyahsari Hanschke, Dylan Rees, Merve Alanyali
IUI 2021
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Yi Wang, Ban Kawas
CHI 2021
Glinda: Supporting Data Science with Live Programming, GUIs and a Domain-specific Language
Robert A DeLine
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