Detection/Identification of Reviewer Expertise and Grant Application Content

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-07 (month precision)Source: Jul-25

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Administrative Functions

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI is designed to detect and identify grant application content and reviewer expertise such as pediatric or other domains to support accurate matching and more efficient, informed decision-making

Expected benefits

The tool is expected to enhance the agency’s mission by improving the accuracy, fairness, and efficiency of processes such as reviewer assignment and grant application analysis. By automating the detection of relevant content and expertise, it reduces manual workload, ensures better alignment between reviewers and applications, and supports more informed decision-making. This leads to more equitable and effective funding outcomes, ultimately benefiting the general public through improved support for research and programs that address critical needs.

System outputs

The AI system’s outputs are classifications or labels indicating whether a grant application involves specific content areas (e.g., pediatric or other domains) and assessments of reviewer expertise based on biosketches, publications, and related data. These outputs are used to support accurate matching between applications and qualified reviewers.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

System name(s)

CSR Detection/Identification of Reviewer Expertise and Grant Application Content (CSRDREAC)

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

This AI use case does not involve training or fine-tuning models. Instead, it uses predefined rules and prompts to analyze existing grant application texts and reviewer information to identify relevant content and expertise. Evaluation is based on validating the accuracy of these prompt-based classifications against known examples.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported