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Completed STANDARD GRANT National Science Foundation (US)

I-Corps: Trackable Reasoning and Analysis Software for Collaboration and Evaluation

$500K USD

Funder National Science Foundation (US)
Recipient Organization Syracuse University
Country United States
Start Date Mar 15, 2021
End Date Jul 31, 2023
Duration 868 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2002287
Grant Description

The broader impact/commercial potential of this I-Corps project is the development of a software application that intelligence analysts can use to improve the quality and accuracy of their analyses. Errors in critical reasoning are detrimental to data analyses. Furthermore, the reports often do not provide readers with a clear sense of the analyst's work, which can result in poor decision-making.

Past approaches to address these issues have had limited success and do not cover each step in the analytical process. The proposed technology offers a more flexible reasoning approach by identifying key information, determining the best path forward, and composing a quality final report that enhances the analysts' reasoning and provides decision-makers with a clear sense of the analyst's process.

By transforming the proposed technology into a commercially viable application, the likelihood of intelligence failures could be reduced as decision-makers access the necessary analyses to pursue policies and strategies that promote national security. This application could also serve as the foundation for additional research and commercial technologies oriented towards helping analysts working in other fields, such as law enforcement, forensics, corporate security, insurance, and finance.

This I-Corps project builds from the team's prior research on developing a state-of-the-art technology to help national intelligence analysts engage in better reasoning and report writing. Intelligence analysts often struggle to ensure that their research and reasoning avoid critical errors, such as cognitive biases. Past approaches to address errors have had limited uptake because analysts find them burdensome and unappealing.

Current approaches do not cover each step in the analytical process. The proposed approach represents an end-to-end workflow solution, that offers a more flexible reasoning approach by identifying key information, determining the path forward, and composing a quality final report that provides decision-makers with a clear sense of the analyst's process.

This technology offers report templates and several optional tools that help analysts process and evaluate information, offload their memory and reasoning work to the application, and share that reasoning effortlessly with collaborators.

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.

All Grantees

Syracuse University

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