DOI · DOI-0077 · record R2676

ATLAS

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: Document processing and extraction Derived by keyword rules; see methodology

Problem the AI is intended to solve

Literature review and data compilation is the most time-consuming phase of the mineral resource assessment workflow (https://www.usgs.gov/media/images/usgs-mineral-resource-assessment-workflow). Identifying and extracting datasets referenced in historical documents, journal articles and published databases is currently done using traditional methods of browsing the web, downloading manuscripts and datasets, and extracting relevant data. The AI system will automatically extract metadata about datasets from manuscripts and make it available to users in a catalog. By supporting user queries of the extracted metadata, the system will enable assessment scientists to identify data sources much more quickly. The tool is intended to support other AI tools down the line that facilitate data extraction and synthesis. The system will also be useful for tracking lineage and usage of published datasets, helping to assure data quality and assess impact.

Expected benefits

The AI will accelerate the mineral resource assessment process and provide a model for other types of resource assessments. By improving efficiency, accuracy, and transparency of assessment workflows, AI tools can help USGS deliver results more rapidly

System outputs

A catalog of metadata about published datasets that supports user queries through agentic AI.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

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

5 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 consultationNo answer reported

Potential impacts and how they were identified

Not reported