Internal Referral Module (IRM)
Description
Automated Assignment of Grants to Program Officers
Detailed example
The outputs are referrals to Program Officers, Program Class Codes, Organizational units - Divisions and Branches and Scientific Research Clusters.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The original IRM application grew out of a desire to refer applications to the appropriate Program Officer to manage the scientific research that fit their portfolio. This manual referral of grant applications still exists within IRM and has been complemented by use of AI/NLP capabilities.
Audit / financial statement impact
The output from this AI use case does not drive any agency decision, including the categories listed in section 6 of the memorandum. The output is a recommendation to assign a grant application to the appropriate Agency staff based on the scientific content of the application. The person to whom the grant application has been referred to can accept, reject, or reassign the application based on their expertise.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
We use eRA grant application data for all fine tuning and optimization for the models. Specially, we extract the title, abstract, specific aims and public health narrative to train our models for prediction.