CLEMSON S.C. (WSPA) — Clemson University’s TRACE Research group used human-centered artificial intelligence research to find how artificial intelligence systems complement how people learn, work and live.

The research group studies human motives, needs, and wants and then takes those characteristics to find out how artificial intelligence can positively support people in their workplace. 

“We do a lot of research on how all of these different areas of research – the social, the computing, the biological – merge to understand what an AI system needs to present, what it needs to act like, what data it needs access to, and how it should respond to that data,” said Flathmann. 

According to TRACE, they find information on how humans interact with AI systems in the first place.

“Your personality results in how you view an AI system. Are you prone to even accepting technology, just as are you prone to accepting certain types of humans? It goes into technology as well,” said Nathan McNeese. 

A Human Teaming Artificial Intelligence System that TRACE conducted research on is Spot, a robo dog, a commercial system from Boston Dynamics. 

Spot is mostly used at construction and manufacturing facilities. It can map its environment, gather data, detect radiation and perform other autonomous work. 

“A lot of people might go into the perception of AI thinking that they might be fearful of it and it might have a negative outcome. But if we really focus on the human aspects of AI, benefiting people with AI systems, the outcomes, the social good that we can create from it is just immeasurable. The goal for human teaming AI systems is to complement what humans are already doing, not take away jobs,” said Chris Flathmann.

Nathan McNeese, associate professor of human centered computing, said there are things AI should and should not be relied on for. 

“You want to rely on AI for things that are very concrete where you have very good data that you’re confident in what that data is and that the data will allow for a very concrete outcome to be produced. Where we don’t want to rely on AI systems is for things that we’re good at, humanistically speaking, creativity and emotions,” said McNeese.

The TRACE Research group said one of the big takeaways they found is that humans have different levels of acceptance toward AI systems. They are working on ways to improve how people view artificial intelligence by bringing the technology to local schools for students to see and learn about.