Our Work Is Local, but Our Impact Is Global

Seeks to build AI systems that will link city datasets to improve the design, development, and management of smart cities, using the City of Austin as a model.

A lack of affordable housing is a major problem in US cities from the Bay Area to Boston. Austin is no exception. In 2015, the Austin-Round Rock metropolitan area was named one of the most economically segregated areas in the country.

This project reports on how media representations shape public perceptions of AI and then uses findings to explore how Good Systems might better represent everyday interactions with AI to the public. 

Builds technical, legal, and social approaches to maximize the trusted use of camera-generated video data

This project develops methodology and workflows for libraries, archives, and museums to use machine learning and supercomputing resources to generate metadata for AV materials in the humanities. 

This project investigates comparative policies around the creation and use of video data in the public sector. As more cities deploy monitoring and sensing technologies, cameras are in the front lines of data-gathering in traffic, policing, and health and safety.

Older adults are especially vulnerable to believing and circulating disinformation online, and we want to enable this population to use social media more responsibly.

Explores racial disparities in AI-based systems and seeks to design and implement solutions in the areas of public administration, transportation, and health.

This project designs and prototypes new ways to find, interpret, and evaluate online information with the goal of helping to combat rampant misinformation. It studies how people evaluate and integrate information from disparate online sources, focusing on fact-checking as it relates to the COVID-19 pandemic. 

This project examines how human-centered approaches to assess bias and fairness can address a critical gap to inform research on algorithmic fairness.