DOE · DOE-632 · record R3489
Invoice Scanning System
Department of Energy · RL - Hanford - Richland Operations Office - Hanford (EM)
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
- This AI system does not provide a principle basis for decisions or actions with legal, material binding, or have any significat effects of those items listed in OMB-25-21 in the definition of High Impact.
- Start date
- 2024-09-04 (day precision)Source: 2024-09-04T00:00:00
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Procurement and Financial Management
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
Missing information in subcontract submittals
Expected benefits
greater accuracy of information on required forms
System outputs
Analytics
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)
- Hanford Accreditation Boundary (HAB)
- 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
- Subcontractor Database
- Federal Data Catalog
- Not reported
Governance
7 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Pre-deployment testingReported in placeSource: a) Yes
AI impact assessmentNo answer reported
Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development
Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established
Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established
Fail-safeNot applicableSource: b) Not applicable
Appeal processNot applicableSource: b) Not applicable
User and public consultationReported in placeSource: a) Direct usability testing
Potential impacts and how they were identified
Impacts were assessed by the software owner and developer