SOO_PHIS_AI.pdf

US Department of Agriculture (USDA)

Statement of Objectives

PHIS Predictive Analytics

Contents

Project Title

1.0 Purpose

2.0 Scope or Mission

3.0 Period and Place of Performance

4.0 Background

5.0 Performance Objectives

6.0 Deliverables / Schedule

7.0 Operating Constraints

8.0 Attachments

Project Title PHIS Predictive Analytics

1.0 Purpose

To design and implement a state-of-the-art AI-powered decision-support platform that transforms FSIS’s ability to prioritize inspections, allocate resources, and oversee food safety operations using inspection data, LLMs, and external risk indicators.

2.0 Scope or Mission

The platform will: Incorporate AI-driven risk scoring that integrates risk signals from structured data elements and narrative-based signals. Integrate external data sources such as weather, illness trends, and recall history. Provide role-specific dashboards and conversational AI tools for supervisory, analytical, and operational users. Enable predictive resource planning, scenario simulation, and real-time alerts. Ensure transparency, governance, and user trust through explainable AI and audit mechanisms. Allow review of platform scoring decisions by business SMEs and accept feedback to modify scoring decisions, factor weights, etc.

3.0 Period and Place of Performance

TBD

4.0 Background

The Food Safety and Inspection Service (FSIS) seeks to modernize its prioritization and resource allocation processes through advanced artificial intelligence (AI) technologies. Current methods rely heavily on numeric risk scores and manual analysis, which limit agility and predictive capabilities. By leveraging large language models (LLMs), environmental intelligence, and integrated dashboards, FSIS can enhance decision-making, improve food safety outcomes, and optimize operational efficiency.

5.0 Performance Objectives

• Develop an AI-Driven Risk Prioritization Engine

• Develop risk scores for regulated facilities based on consideration of structured data elements and LLM-based narrative analysis for comprehensive risk assessment.

• Integrate external data sources to dynamically adjust risk scores.

• Provide transparent rationale and data provenance for all AI-generated recommendations.

• Enable Advanced Analytical Capabilities

• Implement sentiment and contextual analysis on inspection narratives, consumer complaints, and MOIs.

• Deploy pattern detection algorithms to identify recurring issues across establishments.

• Incorporate predictive resource planning and scenario simulation for strategic decisions.

• Allow visibility of risk scoring decisions for business SMEs and accept feedback to modify model decision parameters.

• Create Role-Specific User Interfaces and Tools

• Design dashboards tailored to supervisory, analytical, and operational roles.

• Include features such as AI-prioritized action queues, risk decomposition panels, and interactive heatmaps.

• Provide conversational AI assistants for natural language queries and real-time insights.

• Enhance Operational Efficiency and Collaboration

• Integrate AI-powered chatbots for summarization, prioritization, and decision support.

• Enable cross-role collaboration through tagging, co-authoring simulations, and shared insights.

• Support mobile and field applications for inspectors to access assignments and AI alerts on the go.

• Ensure Trust, Transparency, and Governance

• Implement model confidence indicators and explainable AI panels.

• Maintain audit logs, user permissions, and feedback loops for continuous improvement.

• Provide gamified training and sandbox environments for user onboarding and skill development.

6.0 Deliverables / Schedule

Key Deliverables

Item No.

Deliverable Objective Due

1 AI Risk Prioritization Engine with integrated narrative and numeric scoring.

Develop an AI-Driven Risk Prioritization Engine

No later than five (5) business days after contract award

2 Role-Specific Dashboards for supervisory, analytical, and operational users.

Develop risk scores for regulated facilities based on consideration of structured data elements and LLM-based narrative analysis for comprehensive risk assessment.

No later than ten (10) business days after contract award

3 Conversational AI Assistant embedded in dashboards and mobile applications.

Integrate external data sources to dynamically adjust risk scores.

No later than ten (10) business days after contract award

4 Scenario Simulation Tools for resource planning and environmental impact forecasting.

Provide transparent rationale and data provenance for all AI-generated recommendations.

No later than ten (10) business days after contract award

5 Governance Framework includes audit logs, transparency indicators, and feedback mechanisms.

Enable Advanced Analytical Capabilities No later than ten (10) business days after contract award

7.0 Operating Constraints

The preferred deployment environment for this solution is the Azure Government Cloud. Proposals that utilize a different environment should include additional hosting environment cost estimates for those alternatives.

8.0 Attachments

Success Metrics

• Accuracy: Improved precision in identifying high-risk establishments.

• Efficiency: Reduction in time spent on manual prioritization and reporting.

• Adoption: High user engagement across roles and positive feedback on usability.

• Impact: Demonstrable improvement in food safety outcomes and resource optimization.

• Transparency: Clear AI rationale and confidence indicators are accessible to all users.