HHS · no agency ID · record R0930

NICHD RPAB AI/ML Application Referral System

Department of Health and Human Services · HHS/NIH

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: Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact
Impact justification: The primary objective is to improve the efficiency, accuracy, and consistency of grant application referral assignments while streamlining the internal process for referring new applications. The AI-generated output supports subject matter experts by providing additional information that helps them make faster decisions and prioritize applications for review. All AI output is used solely as an assistive tool, and every referral decision undergoes 100% human review. Therefore, this use case does not meet the definition of a high-impact AI.
Start date: 2024-08 (month precision)Source: Aug-24
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 primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications.

Expected benefits

This AI use case increases the efficiency of the grant referral process and reduces overlapping efforts in grant referral review.

System outputs

Results are presented as class predictions and class probabilities as recommendations for branch assignment.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): hdrpabappit01, hdrpabappit02
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: NIH IMPAC II funded and unfunded grant application data is used. Unstructured text from project abstract, specific aims, and title are encoded and vectorized for model training and inference. Fiscal year, activity code, and RCDC terms are transformed via one-hot encoding for use in model training and inference. PII related to individuals associated with the grant is kept intact to preserve the integrity of the use case of grant application referral and the trends of researchers' focus on particular scientific areas.
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported