A recent study reveals that people may be more willing to trust computers than their fellow human beings, especially if a task becomes too challenging.
The findings by scientists at the University of Georgia were published in the journal Nature‘s “Scientific Reports.”
From choosing the next song on your playlist to choosing the right pant size, people are relying more and more on the advice of algorithms in help to make everyday decisions and streamline their lives.
“Algorithms are able to do a huge number of tasks, and the number of tasks that they are able to do is expanding practically every day,” said Eric Bogert, a PhD student in the Terry College of Business Department of Management Information Systems.
Bogert added, “It seems like there’s a bias towards leaning more heavily on algorithms as a task gets harder and that effect is stronger than the bias towards relying on advice from other people.”
Bogert worked with management information systems professor Rick Watson and assistant professor Aaron Schecter on the paper who said that “Humans rely more on algorithms than social influence as a task becomes more difficult.”
Their study, which involved 1,500 individuals evaluating photographs, is part of a larger body of work analyzing how and when people work with algorithms to process information and make decisions.
For this study, the team had volunteers count the number of people in a crowd in a photograph and supplied suggestions that were generated by a group of other people while other suggestions were generated by an algorithm.
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As the number of people in the photograph expanded, counting became more difficult and people were more likely to follow the suggestion generated by an algorithm rather than count themselves or follow the “wisdom of the crowd,” Schecter said.
Schecter explained that the choice of counting as the trial task was an important one because the number of people in the photo makes the task objectively harder as it increases. It is also the type of task that laypeople expect computers to be adept at.
“This is a task that people perceive that a computer will be good at, even though it might be more subject to bias than counting objects,” Schecter said.
Facial recognition and hiring algorithms have come under scrutiny in recent years, as well, because their use has revealed cultural biases in the way they were built which can cause inaccuracies when matching faces to identities or screening for qualified job candidates, Schecter said.
Those biases may not be present in a simple task like counting, but their presence in other trusted algorithms is a reason why it’s important to understand how people rely on algorithms when making decisions, he added.
This study was part of Schecter’s larger research program into human-machine collaboration, which is funded by a $300,000 grant from the U.S. Army Research Office.
“The eventual goal is to look at groups of humans and machines making decisions and find how we can get them to trust each other and how that changes their behavior,” Schecter said.
He adds that “Because there’s very little research in that setting, we’re starting with the fundamentals.”