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Research Spending & Results

Award Detail

Awardee:CLAUDIUS LEGAL INTELLIGENCE INC
Doing Business As Name:CLAUDIUS LEGAL INTELLIGENCE INC
PD/PI:
  • Vinicius Silvestrin Pantoja
  • (609) 937-1897
  • vinicius@claudius.ai
Award Date:07/23/2021
Estimated Total Award Amount: $ 256,000
Funds Obligated to Date: $ 256,000
  • FY 2021=$256,000
Start Date:08/01/2021
End Date:01/31/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: Artificial Intelligence Tool for Analysis of Legal Documents
Federal Award ID Number:2112315
DUNS ID:117743212
Program:SBIR Phase I
Program Officer:
  • Peter Atherton
  • (703) 292-8772
  • patherto@nsf.gov

Awardee Location

Street:309 TRINITY CT APT 11
City:PRINCETON
State:NJ
ZIP:08540-7029
County:Princeton
Country:US
Awardee Cong. District:

Primary Place of Performance

Organization Name:CLAUDIUS LEGAL INTELLIGENCE INC
Street:
City:
State:NJ
ZIP:08540-7029
County:Princeton
Country:US
Cong. District:12

Abstract at Time of Award

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to establish an artificial intelligence (AI) system capable of providing data-driven insights for attorneys. The legal community currently lacks data analysis tools to help with civil case preparation, which can lead to suboptimal trial outcomes. The proposed technology can help lower costs through document analysis. The technology is designed to both automate and improve the decision-making process and enable attorneys to expand their case load, as well as enabling cost-effective representation. This Small Business Innovation Research (SBIR) Phase I project will use federated learning techniques to train the technology’s algorithm across multiple decentralized databases without exchanging data samples, thus keeping information private and confidential. This approach overcomes the lack of access problem in applying AI to legal cases, without compromising data confidentiality. The proposed research will include two major objectives: 1) improve and verify the accuracy of the platform, and 2) create internal checks to ensure that the model does not propagate bias. Computational outputs will be assessed using data and records from randomly selected cases with known outcomes to demonstrate system accuracy; moreover, the model will explicitly account for potential sources of bias. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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