HHS · no agency ID · record R0916

Federal IT Acquisition Reform Act (FITARA) Tool

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: 2017-01 (month precision)Source: Jan-17
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

Contracting officers use this tool to help identify whether a Statement of Work meets the criteria of Federal IT Acquisition Reform Act (FITARA).

Expected benefits

Contracting Officers can use this tool to indicate if Statements of Work are likely to be IT-related, which saves significant manual effort and time required to identify relevant contracts.

System outputs

User uploads a contract SOW and the FITARA Tool processes and predicts the likelihood on whether or not FITARA applies with a confidence score. The output data from the tool is displayed via a custom module.

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): FITARA
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 dataset was curated using NIAID SOWs which were manually labelled to train the classification model.
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