Machine-learning algorithms are used to find patterns in data that humans wouldn’t otherwise notice and are being deployed to help inform decisions big and small.
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 AI.
“We don’t know much about how nonexperts in machine learning come to learn algorithmic tools,” said Swati Mishra, a Ph.D. student in the field of information science.
As machine learning has entered fields and industries traditionally outside of computing, the need for research and effective, accessible tools to enable new users in leveraging artificial intelligence is unprecedented, Mishra said.
Mishra’s latest research – including the development of her own 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.
“By intentionally focusing on appropriating existing models into new tasks, Swati’s work helps novices not only use machine learning to solve complex tasks, but also take advantage of machine-learning experts’ continuing developments,” said Jeff Rzeszotarski.
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