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Texas A&M Lands $1.6 Million to Study Algorithmic Decision Making

Texas A&M Lands $1.6 Million to Study Algorithmic Decision Making 

Researchers at Texas A&M University have landed a $1.6 million grant from the Defense Advanced Research Projects Agency (DARPA) to investigate algorithmic reasoning.

The researchers are looking into algorithms that use large data sets and machine learning to refine and improve the answers they deliver over time, as they encounter new data and receive feedback about past recommendations.

Sometimes, as in cases where algorithms offer suggestions for patient diagnosis, treatment or chronic disease management, it's important for users to understand why a particular recommendation is being made.

"An end user who understands the 'why' of an algorithm's recommendation is better able to trust its results and use them to confidently make decisions," said Eric Ragan, an assistant professor of visualization at TAMU and co-principal investigator on the project, in a report on the grant. "People don't want to blindly accept a computer's recommendations if they don't understand where they came from."

Ragan and his collaborators are modeling the steps an algorithm performs as it generates a recommendation, and then creating visualizations of those models to help end users understand the process better.

"Our goal is to make simple visual designs like bar charts," said Ragan. "Tentative plans are to test the effectiveness of the charts to show the most important information in the model. Then we'll create graphics to represent data in more detail. Ultimately, we're going to test a variety of representations."

The project is one of 13 that DARPA is funding relating to artificial intelligence and algorithmic reasoning in an effort to help military end users better understand the recommendations of algorithms.

About the Author

Joshua Bolkan is contributing editor for Campus Technology, THE Journal and STEAM Universe. He can be reached at [email protected].

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