UT researchers Maria De-Arteaga and Min Kyung Lee talk about their different but complementary work to make algorithms less biased and harmful.

The University of Texas at Austin is one of many academic institutions working with community volunteers in Maryland to help teach social media how to respond to disasters.

Last year, Maria De-Arteaga joined the McCombs School of Business faculty as an assistant professor in the Information, Risk and Operations Management Department. During her Ph.D., she became increasingly concerned about the risk of overburdening or underserving historically marginalized populations through the application of machine learning. She's now devoted her career to understanding the risks and opportunities of using ML to support decision-making in high-stakes settings.

When we think of the robots in practical use today, the most common are stationary robots that help assemble parts in automotive factories or can assist in performing delicate medical surgeries. Building a robot that can move within the human world with all its unpredictable variables, like self-driving cars, is oftentimes more difficult.

As digital assistants like Siri and Alexa become more common in our lives, people increasingly see them as companions that accompany them throughout their day. Young children, especially, are more apt to see these devices as real people or friends.

University of Texas researchers argue that information scientists have a bigger role to play in the COVID-19 crisis because of the proliferation of conflicting messages. We asked three experts, who have been sheltering-in-place for the past two months in Austin, Texas, to tell us more about their perspectives on the pandemic.

When COVID-19 began actually infecting bodies around the globe earlier this year, UT community and regional planning associate professor Junfeng Jiao decided to look at the transportation system as a way to predict the virus’ spread.