Knowing AI A Better Way

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Now a days, busy hospitals use Artificial Intelligence system to help diagnose medical conditions based on patients’ X-ray images. Despite the AI system helping to make faster diagnoses, how does one know when to trust the AI’s predictions? Well, the researchers at the Massachusetts Institute of Technology have created a technique that guides humans to a more accurate understanding of when a machine makes correct predictions and when it makes incorrect ones.

Mostly the doctors and others rely on their expertise even though they depend on AI for any diagnosis. However, the mew resarch supported by the U S National Science Foundation could help humans make better decisions or come to conclusions faster when working with AI agents.

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Hussein Mozannar of MIT said; “we propose a teaching phase where we gradually introduce humans to this AI model so they can see its weaknesses and strengths.”

“We do this by mimicking the way people will interact with AI in practice, but we intervene to give them feedback to help them understand each interaction,” the researcher said.

Mozannar conducted the research with Arvind Satyanarayan and David Sontag, also of MIT. The findings will be presented at the Association for the Advancement of Artificial Intelligence Conference in February The work focuses on the mental models humans build. If the radiologist, for example, is not sure about a case, she may ask a colleague who is an expert in a certain area. From experience and her knowledge of this colleague, she has a mental model of his or her strengths and weaknesses that she uses to assess the advice, the NSF said in an official release.

Humans build the same kinds of mental models when they interact with AI, so it’s important that those models are accurate, Mozannar says. Cognitive science suggests that humans make decisions about complex tasks by remembering past interactions and experiences. So, the researchers designed a process that provides examples of a human and AI working together, which serve as reference points a person can draw on in the future.

“This work is an ideal example of how mathematical results can be brought to bear on solving real-world problems in AI,” said Rance Cleaveland, director of NSF’s Division of Computing and Communication Foundations. “The interplay between basic research and its applications is a hallmark of the kinds of impactful results NSF is looking for.”

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