USDA · USDA-043 · record R1345

Genomic Analyses of Pathogen Subtypes

Department of Agriculture · Food Safety

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2024-10-01 (day precision)Source: 10/1/2024
Withheld from public reporting: Not reported

Mission

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

Problem the AI is intended to solve

The purpose of this use case is to use machine learning (ML) methods to group foodborne bacteria based on patterns in their genes, then connect this information with available health data to evaluate foodborne illness risk to public health.

Expected benefits

Expected benefits include improving our understanding of foodborne illness, identifying key genes and trends, and evaluating those that are important for public health.

System outputs

The model outputs predictions of foodborne illnesses with high public health risk, key genetic markers by importance, and emerging trends.

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: 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: No
Demographic features: c) Age
Training and evaluation data: The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS).
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