Designing Responsible AI Technologies to Curb Disinformation

 

The rise of social media and the growing scale of online information have led to a surge of intentional disinformation and incidental misinformation. It is increasingly difficult to tell fact from fiction, and the challenge is more complex than simply differentiating “fake news” from simple facts. This project uses qualitative methods and machine learning models to understand how digital disinformation arises and spreads, how it affects different groups in society, and how to design effective human-centered interventions.

 

Team Members


News


Nov. 9, 2022
Disinformation Day 2022 Considers Pressing Need for Cross-sector Collaboration and New Tools for Fact Checkers
Good Systems Researchers and Partners Explore Issues of Bias, Fairness, and Justice and Examine Challenges and Opportunities for Fact Checkers at the Inaugural Disinformation Day Conference.
March 5, 2022
Challenging the Status Quo in Machine Learning
UT researchers Maria De-Arteaga and Min Kyung Lee talk about their different but complementary work to make algorithms less biased and harmful.
Jan. 29, 2021
Machine Learning for Social Good
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.

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