Our Work Is Local, but Our Impact Is Global

AI technologies can help build smarter cities by using data to improve services, such as public safety, transit systems, and emergency response. They can also help us evaluate how new infrastructure, such as public transportation lines, will affect access to housing, jobs, and public services.

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. 

Cities are adopting camera technologies, including public video cameras and sensors, that use AI to process visual data with the promise of improving services, enhancing management capabilities, and lowering costs. 

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.

AI-based technologies used by cities and organizations have been shown to exhibit racial bias, and yet, they can determine who gets access to employment, care, and adequate housing.

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.