HHS · no agency ID · record R0941

SRDMS NLP COI

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: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2019-01 (month precision)Source: Jan-19
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

Scientific review officers need to identify individuals who may pose a conflict-of-interest (COI) during the grant application review process.

Expected benefits

SRDMS NLP COI is able to automate the identification of individuals who may pose a conflict-of-interest during the grant application review process, which saves significant time and effort by the SRO during application review and promotes consistency in identifying COIs.

System outputs

Tool ingests grant application PDFs from an upstream source system, eRA, and these applications are processed to extract relevant name entities. The tool returns extracted name entities and metadata in a table that are displayed via custom module within the SRDMS application.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): SRDMS NLP COI KP/KI Module
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Deloitte
Vendors (standardized): Deloitte

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: A labeled dataset of grant applications and associated conflicts of interest is used to calculate pipeline evaluation metrics.
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