USDA · USDA-035 · record R1338

Census of Agriculture Propensity Scores via Machne Learning

Department of Agriculture · Research, Education and Economics

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: 2022-10-01 (day precision)Source: 10/1/2022
Withheld from public reporting: Not reported

Mission

Topic area: Service Delivery
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The Census of Agriculture is collected every 5 years and requires high participation levels from individuals and agricultural operations to provide relevant agricultural data to the nation.

Expected benefits

This model predicts how likely individuals or operations are to complete the Census of Agriculture. The predictions can help data collectors decide where they need to focus their efforts in order to get more complete census responses.

System outputs

The model outputs a probability score (all values from and including 0 to 1).

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): NASS REE CIPSEA Citrix Env
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: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: c) Age; i) Income
Training and evaluation data: Information on the National Agriculture Statistics Service (NASS) list frame including previous survey response information, demographic information, as well as geospatial information.
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