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—from COVID-19 vaccination development to Netflix recommendations.
July 30,2021 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—from COVID-19 vaccination development to Netflix recommendations. New award-winning 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 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. “The reason is that there’s a hype that’s developed that suggests machine learning is for the ordained.” Mishra is lead author of “Designing Interactive Transfer Learning Tools for ML Non-Experts,” which received a Best Paper Award at the annual ACM CHI Virtual Conference on Human Factors in Computing Systems, held in May. 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. Existing research into these interactive machine-learning systems has mostly focused on understanding the users and the challenges they face when navigating the tools.
