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. 

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.

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

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 the temporal dynamics of emotional appeals in Russian campaign messages used in the 2016 election on Facebook and Twitter. 

This project investigated how data ethics can be a point of departure in designing and evaluating good systems, examining the contradictions and pressure points among various data practices. 

This project examined how children from groups underrepresented in STEM programs understand, interact with, and evaluate AI-driven digital assistants. 

Information and cultural heritage professionals have become increasingly interested in using data science, machine learning, and AI applications in the management of data and operations at libraries, archives, museums.

Artificial intelligence systems increasingly automate and assist people in making managerial and governance decisions. AI manages worker routines and tasks, determines how to distribute resources within cities, and assists in transportation management.

One of the main tasks of any city government is to keep infrastructure such as roads, signs, accessibility ramps, and sewers in perpetual working order. The first step of maintenance is the timely identification of a problem. However, many infrastructure defects are left undetected and unattended for long periods of time

The Austin History Center, the City of Austin, and UT Libraries maintain substantial collections of scanned maps, which are needed for historical reference when conducting research or planning new projects. Unfortunately, these scanned maps can be difficult to use due to the challenges involved with discovering, processing, and analyzing map images in their current digitized form.

Optimizing ambulance allocation and routing is one of the most efficient ways for EMS to save more lives at virtually no cost. However, current EMS software was developed under models that assume normal demands.

Postpartum mood and anxiety disorders affect as many as one in five women globally. Left untreated, these can have major negative effects on maternal and child health and well-being. While effective treatments are available, nearly 60% of mothers with symptoms are undiagnosed, and 50% of diagnosed mothers are left untreated.

This project addresses the conflict between convenience and privacy inherent to computer vision with the goal of developing future computer vision technologies that support diverse users with visual impairments, especially those who are traditionally underserved.   

This project considers high confidence policy learning as an evaluation criterion for good systems and investigates new algorithms for meeting this criterion. 

We are working with several City of Austin offices to confront the problem of homelessness. People experience homelessness as a continuum, ranging from housing instability to incarceration, couch-surfing to street homelessness, and episodic to chronic.

The goal of this project is to build an AI system using crowd-sourced data to help predict the health impacts of different neighborhood environments.