Fernanda Viégas, a professor of computer science at Harvard University, who didn’t take part in the study, says she is happy to see a fresh tackle explaining AI systems that not only offers users insight into the system’s decision-making process but does so by questioning the logic the system has used to achieve its decision. 

“Provided that one in every of the primary challenges within the adoption of AI systems tends to be their opacity, explaining AI decisions is essential,” says Viégas. “Traditionally, it’s been hard enough to elucidate, in user-friendly language, how an AI system involves a prediction or decision.” 

Chenhao Tan, an assistant professor of computer science on the University of Chicago, says he would really like to see how their method works in the actual world—for instance, whether AI may also help doctors make higher diagnoses by asking questions.

The research shows how essential it’s so as to add some friction into experiences with chatbots so that individuals pause before making decisions with the AI’s help, says Lior Zalmanson, an assistant professor on the Coller School of Management, Tel Aviv University. 

“It’s easy, when all of it looks so magical, to stop trusting our own senses and begin delegating every little thing to the algorithm,” he says. 

In one other paper presented at CHI, Zalmanson and a team of researchers at Cornell, the  University of Bayreuth, and Microsoft Research, found that even when people disagree with what AI chatbots say, they still are inclined to use that output because they think it sounds higher than anything they might have written themselves.

The challenge, says Viégas, will probably be finding the sweet spot, improving users’ discernment while keeping AI systems convenient. 

“Unfortunately, in a fast-paced society, it’s unclear how often people will want to interact in critical considering as a substitute of expecting a ready answer,” she says.