Research Projects

  
Filtered by: Other Non-Federal

 

Collaborative Research: Using Artificial Intelligence To Improve Administration of the Freedom of Information Act (FOIA)
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding with MITRE Corporation to research the application of various forms of artificial intelligence (AI) including machine learning (ML) methods to aid in the redaction of documents corresponding to one or more FOIA exemptions.
Information Technology Access RERC
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Non-Federal

Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
Mitigating online COVID misinformation costs: From individual to field interventions
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.

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