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Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation
Building a machine learning model in a sophisticated domain is a time-consuming process, partially due to the steep learning curve of domain knowledge for data scientists.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others.
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.
How Much Automation Does a Data Scientist Want?
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
