People may Trust Computers more than Humans

Despite increasing concern over the intrusion of algorithms in daily life, people may be more willing to trust a computer program than their fellow humans, especially if a task becomes too challenging, according to new research from data scientists at the University of Georgia.

From choosing the next song on your playlist to choosing the right size pants, people are relying more on the advice of algorithms to help 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 Ph.D. student in the Terry College of Business Department of Management Information Systems. "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, "Humans rely more on algorithms than social influence as a task becomes more difficult," which was published April 13 in Nature's Scientific Reports journal.

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 asked volunteers to count the number of people in a photograph of a crowd and supplied suggestions that were generated by a group of other people and suggestions generated by an algorithm.

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 also is the type of task that laypeople expect computers to be good 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. "One of the common problems with AI is when it is used for awarding credit or approving someone for loans. While that is a subjective decision, there are a lot of numbers in there -- like income and credit score -- so people feel like this is a good job for an algorithm. But we know that dependence leads to discriminatory practices in many cases because of social factors that aren't considered."

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. "Because there's very little research in that setting, we're starting with the fundamentals."

Schecter, Watson and Bogert are currently studying how people rely on algorithms when making creative judgments and moral judgments, like writing descriptive passages and setting bail of prisoners.

Bogert E, Schecter A, Watson RT
Humans rely more on algorithms than social influence as a task becomes more difficult.
Sci Rep 11, 8028 (2021). 10.1038/s41598-021-87480-9.

Most Popular Now

MEDICA 2024 + COMPAMED 2024: Adapted Hal…

11 - 14 November 2024, Düsseldorf, Germany. The final preparations for MEDICA 2024 and COMPAMED 2024 in Düsseldorf have begun. A total of more than 5,500 exhibitors from approximately 70 countries...

AI does Not Necessarily Lead to more Eff…

The use of artificial intelligence (AI) in hospitals and patient care is steadily increasing. Especially in specialist areas with a high proportion of imaging, such as radiology, AI has long...

Commission Joins Forces with Venture Cap…

The Commission has launched a Trusted Investors Network bringing together a group of investors ready to co-invest in innovative deep-tech companies in Europe together with the EU. The Union's investment...

Why the NHS is Seeking to Make Media Ser…

Opinion Article by Dean Moody, Healthcare Services Director, Airwave Healthcare. Tim Kelsey and Martha Lane Fox called for WiFi to be made available free of charge throughout the NHS back in...

An AI-Powered Pipeline for Personalized …

Ludwig Cancer Research scientists have developed a full, start-to-finish computational pipeline that integrates multiple molecular and genetic analyses of tumors and the specific molecular targets of T cells and harnesses...

Wearable Cameras Allow AI to Detect Medi…

A team of researchers says it has developed the first wearable camera system that, with the help of artificial intelligence (AI), detects potential errors in medication delivery. In a test whose...

Philips and Medtronic Advocacy Partnersh…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Medtronic Neurovascular, a leading innovator in neurovascular therapies, today announced a strategic advocacy partnership. Delivering timely stroke...

AI could Transform How Hospitals Produce…

A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient...

New AI Tool Predicts Protein-Protein Int…

Scientists from Cleveland Clinic and Cornell University have designed a publicly-available software and web database to break down barriers to identifying key protein-protein interactions to treat with medication. The computational tool...

Great Start for Ideas and Innovations: D…

8 - 10 April 2025, Berlin, Germany. From 15 October to 15 November 2024, the DMEA invites experts from business, science, politics and practice to actively participate in shaping the congress...

Start-Ups will Once Again Have a Starrin…

11 - 14 November 2024, Düsseldorf, Germany. The finalists in the 16th Healthcare Innovation World Cup and the 13th MEDICA START-UP COMPETITION have advanced from around 550 candidates based in 62...

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...