DOL · DOL-21 · record R1291
Expenditure Classification Autocoder
Department of Labor · BLS
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
- Used for survey processing for statistical purposes
- Start date
- 2024-01-01 (day precision)Source: 01/01/2024 00:00:00
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Other: Applied Mathematical SciencesSource: Other - Applied Mathematical Sciences
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
Assigns expense classification categories to reported expenses from Consumer Expenditure Diary Survey respondents.
Expected benefits
Efficiency of expenditure classification efforts
System outputs
BLS-internal item codes (expenditure classification categories)
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)
- BLS Internal System
- 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)
- 1605TA-21-F-00064
- 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
- Not reported
- Training and evaluation data
- Agency Internal; Expenditure descriptions and corresponding item code assignments
- 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