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Monday, July 22, 2024

Explainable Artificial Intelligence Can Enhance Scientific Workflows

When it comes to abstract scientific concepts, machine learning can seem more ambiguous.

Machine learning techniques are increasingly being used in the sciences, as they can streamline work and improve efficiency. But these techniques are sometimes met with hesitation: When users don’t understand what’s going on behind the curtains, they may lack trust in the machine learning models.

As these tools become more widespread, a team of researchers in Lawrence Livermore National Laboratory’s Computing and Physical and Life Sciences directorates are trying to provide a reasonable starting place for scientists who want to apply machine learning, but don’t have the appropriate background. The team’s work grew out of a Laboratory Directed Research and Development (LDRD) project on feedstock materials optimization, which led to a pair of papers about the types of questions a materials scientist may encounter when using machine learning tools, and how these tools behave.

Trusting artificial intelligence is easy when its conclusion is a simple ground truth, like identifying an animal. But when it comes to abstract scientific concepts, machine learning can seem more ambiguous.

“There’s been a lot of work applying machine learning to natural images — cats, dogs, people, bicycles,” said Brian Gallagher, one of the project members and a group leader in the Center for Applied Scientific Computing. “These are naturally decomposable into parts, like wheels or whiskers. Those kinds of explanations aren’t meaningful here; there aren’t subparts to decompose into.”

Read more at Lawrence Livermore National Laboratory

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