DOI · DOI-0110 · record R2638
Inference of PFAS precursor compositions
Department of the Interior · USGS
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- Not reported
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Science
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
This approach will allow for better identification of PFAS source zones and materials, and will enable better estimation of transport processes
Expected benefits
Improved understanding of PFAS compositions, sources, and transport mechanisms will improve mitigation strategies. These improvements have the potential to reduce mitigation costs while improving overall outcomes.
System outputs
Tabular data of predicted PFAS precursor compositions.
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)
- Not reported
- Custom-developed code
- No
- Public source code
- pubs.acs.org ↗
Sourcing
- How it was built
- Not reported
- 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
- No
- Demographic features
- k) None of the above
- Training and evaluation data
- Not reported
- Federal Data Catalog
- Not reported
Governance
6 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 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 processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress
User and public consultationReported in progressSource: e) In-progress
Potential impacts and how they were identified
Not reported