AI/ML, AV/AR, Data Analytics, Data Collection, Data Privacy, Public Safety, User Research
University of Texas, Austin
The City of Austin will be working with S.Craig Watkins (Professor and Director of the Institute for Media Innovation), Sherri R. Greenberg (Professor of Practice), Deepak Chetty (Assistant Professor of Practice), and William Spelman (Professor) from the University of Texas, Austin. The team has proposed a feasibility study of new crowd-data protocols and scenarios to mitigate the impact of bias in crowd management and of VR training modules that support public safety staff in reducing implicit bias in operations. The research will explore the implications of implicit bias in the collection, management and analysis of crowd data and the design of training modules. This partnership is part of the research team’s larger work in the interdisciplinary UT Good Systems initiative, read more about it here: https://bridgingbarriers.utexas.edu/good-systems/
Pre-COVID pandemic, The City of Austin has been home to large-scale events such as South by Southwest, Austin City Limits Music Festival, etc. Public safety and government staff have found organizing and managing events of large sizes to be confusing and chaotic.
Recent public demonstrations around the country and in Austin about policing have further magnified the need to help our public safety organizations improve engagement with the public during large public events. The City of Austin has embarked on a 're-imagining public safety' initiative. As part of that effort, we would like to explore how to better train public safety to respond safely and effectively with our residents and guests.
There are three types of large events to consider: (1) Structured (2) Unstructured (3) Hybrid. Each of those events requires different considerations and plans from city staff.
The issues that large events can create:
The City of Austin seeks to better understand:
The academic team would have access to Austin's:
The city of Austin would like to achieve the following outcomes:
In the short term:
In the long term: