The National Science Foundation’s Division of Civil, Mechanical, and Manufacturing Innovation awarded nearly $70,000 to three universities in 2020 for a collaborative research grant titled “Human-AI Teaming for Big Data Analytics to Enhance Response to the COVID-19 Pandemic.”
According to documents reviewed by The Federalist, the NSF allocated $24,498 to Brigham Young University; $24,266 to George Mason University; and $20,262 to the University of Texas at Austin for the project.
“Social media data can provide important clues and local knowledge that can help emergency managers and responders better comprehend and capture the evolving nature of many disasters,” the project’s abstract states. This funding enabled researchers to study how artificial intelligence algorithms can help authorities track, and potentially act on, American’s online speech.
Intending to advance the field of “human-machine learning” by analyzing how human researchers and AI can collaborate to “comprehend social media patterns during an evolving disaster,” the project used “social media messages” to develop algorithmic surveillance practices. The stated purpose of the research was to help first responders mitigate crises.
The researchers claim their findings will “help emergency managers better train their volunteers who comb through social media using their understanding of the local knowledge and built environment to help machines see new patterns in data.” So, should the artificial intelligence in question lack the ability to interpret regionally distinguishing terms (references to minor landmarks, colloquialisms, etc.), human researchers provide context to fill the gaps.
Keri Stephens, a faculty member in the Moody College of Communication at the
