Research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help nonexperts effectively, efficiently, and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of artificial intelligence.
"There's a hype that's developed that suggests machine learning is for the ordained," says Swati Mishra, a Ph.D. student in computing and information science. As machine learning enters fields outside of computing, the need for research and effective, accessible tools to help new users leverage AI is unprecedented, Mishra says.
Existing research into interactive machine-learning systems has mostly focused on understanding the users and the challenges they face when navigating the tools. Mishra's research — including the development of an interactive machine-learning platform — breaks fresh ground by investigating the inverse: How to better design the system so that users with limited algorithmic expertise but vast domain expertise can learn to integrate preexisting models into their own work.
Misha and Cornell Assistant Professor Jeffrey Rzeszotarski describe the platform in "Designing Interactive Transfer Learning Tools for ML Non-Experts," which received a Best Paper Award at the virtual 2021 ACM CHI Conference on Human Factors in Computing Systems.
From Cornell University
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