Research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help non-experts effectively, efficiently and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of artificial intelligence, Tech Xplore reports. “We don’t know much about how nonexperts in machine learning come to learn algorithmic tools,” said Swati Mishra, a PhD 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. Her latest research – including the development of her own interactive machine-learning platform – breaks 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 existing models into their own work. “If we design machine-learning tools correctly and give enough agency to people to use them, we can ensure their knowledge gets integrated into the machine-learning model,” she said.