A study on typical human mistakes in evaluating images may lead to computer algorithms that help make better decisions about visual information, such as while reading an X-ray or moderating online content.
Researchers from Cornell and partner institutions analyzed more than 16 million human predictions of whether a neighborhood voted for Joe Biden or Donald Trump in the 2020 presidential election based on a single Google Street View image. They found that humans as a group performed well at the task, but a computer algorithm was better at distinguishing between Trump and Biden country.
The study also classified common ways that people mess up, and identified objects – such as pickup trucks and American flags – that led people astray.
“Algorithms can screw up in a myriad of ways and that’s very important,” said Emma Pierson, assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion with the Cornell Ann S. Bowers College of Computing and Information Science and senior author of the study. “But humans are themselves biased and error-prone, and algorithms can provide very useful diagnostics for how people mess up.”
Read the full story on the Cornell Ann S. Bowers College of Computing and Information Sciences website.