AI Usage Policy for Supply Chain Due Diligence

Template for Ethical & Effective AI Implementation

Version
1.0
Effective Date
[Insert Date]
Policy Owner
[Compliance Dept]
Review Cycle
Annual

📋 Table of Contents

1. PURPOSE & SCOPE

1.1 Purpose

This policy establishes guidelines for the responsible use of Artificial Intelligence (AI) systems in supply chain due diligence, risk assessment, and compliance monitoring. The policy aims to:

  • Ensure AI systems enhance, not replace, human decision-making
  • Prevent AI hallucinations and misinformation in compliance assessments
  • Maintain data privacy and confidentiality of supplier audit information
  • Establish accountability for AI-assisted decisions
  • Build trust in AI-generated insights through transparency

1.2 Scope

This policy applies to:

  • All employees, contractors, and third parties using AI systems for supplier evaluation
  • AI tools for risk prediction, audit analysis, and compliance monitoring
  • Systems processing confidential supplier audit reports and due diligence data
  • Vendor relationships where AI processes supplier information

1.3 Definitions

  • AI System: Software that uses machine learning, natural language processing, or predictive analytics to analyze supplier data
  • Hallucination: AI-generated information not supported by source data
  • Source Citation: Reference to specific audit report, finding code, and date supporting an AI claim
  • Human Review Checkpoint: Decision point requiring human verification before action

2. PERMITTED USE CASES ✅ Allowed

AI systems MAY be used for the following supply chain due diligence activities:

2.1 Risk Assessment & Prioritization

  • Predicting supplier risk based on patterns from audited facilities
  • Prioritizing audit schedules based on risk scores
  • Identifying suppliers requiring urgent intervention
Example: "Use AI to predict risk for 10,000 unaudited suppliers based on patterns from 500 completed audits"

2.2 Audit Report Analysis

  • Extracting key findings from audit reports
  • Summarizing compliance issues across multiple suppliers
  • Identifying patterns (e.g., "fire safety violations common in Bangladesh apparel")
Example: "Query AI: 'Which suppliers have child labor risks?' → AI returns list with source citations"

2.3 Research & Information Gathering

  • Looking up local labor laws and regulations
  • Researching industry-specific compliance risks
  • Gathering public information about supplier performance

2.4 Document Preparation

  • Drafting corrective action plan templates
  • Generating supplier communication about compliance issues (with human review)
  • Creating audit schedules and logistics

2.5 Training & Education

  • AI-powered training simulations for auditors
  • Practice scenarios for compliance assessment
  • Knowledge base Q&A systems

3. PROHIBITED USE CASES ❌ Not Allowed

AI systems MUST NOT be used for the following activities:

3.1 Autonomous Decision-Making

  • PROHIBITED: Automatically terminating supplier relationships based solely on AI assessment
  • PROHIBITED: Blacklisting suppliers without human review
  • PROHIBITED: Making legal determinations without legal counsel review

Why: High-stakes decisions require human judgment, accountability, and legal oversight

3.2 Uploading Confidential Data to Public AI Systems

  • PROHIBITED: Pasting supplier audit reports into ChatGPT, Claude, or other public AI tools
  • PROHIBITED: Sharing supplier names, addresses, or audit findings with public AI services
  • PROHIBITED: Using supplier data to train public AI models

Why: Confidentiality violations, potential legal liability, brand reputation risk

3.3 Replacing Human Audits

  • PROHIBITED: Using AI predictions as substitute for actual facility audits
  • PROHIBITED: Certifying supplier compliance without physical inspection
  • PROHIBITED: Approving suppliers for production based solely on AI assessment

Why: AI predicts risk but cannot verify actual conditions; audits remain mandatory

3.4 Accepting AI Output Without Source Verification

  • PROHIBITED: Using AI-generated claims that lack source citations
  • PROHIBITED: Including unsourced AI content in formal reports
  • PROHIBITED: Making decisions based on AI "confidence" alone without reviewing underlying data

Why: Hallucinations are common; every claim must be verified against source documents

4. DATA PRIVACY & SANDBOXING REQUIREMENTS

4.1 Private Infrastructure Mandate

⚠️ CRITICAL REQUIREMENT

All supplier audit data must be processed in private, sandboxed AI environments. Never use public AI systems (ChatGPT, Claude, Gemini) for confidential data.

REQUIRED:

  • All supplier audit data must be processed in private, sandboxed AI environments
  • Use dedicated instances (e.g., private Pinecone namespace, Azure OpenAI) for confidential data
  • Implement access controls limiting AI system access to authorized personnel only

4.2 Data Minimization

  • Upload only necessary data to AI systems (avoid over-sharing)
  • Anonymize supplier names when possible for research queries
  • Delete temporary AI processing data after use

4.3 No Public Model Training

  • Ensure AI vendor agreements prohibit use of our data for model training
  • Verify API settings disable data retention for model improvement
  • Document data processing agreements (DPAs) with all AI vendors

5. SOURCE CITATION REQUIREMENTS

5.1 Mandatory Citation Standard

Every AI-generated claim used in decision-making MUST include:

Required Elements:

  1. Source Document: Audit report filename or ID
  2. Supplier Identification: Supplier ID or facility name
  3. Finding Reference: Specific finding code (e.g., "Finding CL.2")
  4. Date: Audit date or report date
  5. Excerpt: Relevant text from source document
Example of Compliant Citation:
"Lahore Leather Works (Pakistan) has critical child labor risk. [Source: S004_Lahore_Leather_Works_Pakistan_IEA.md, Finding CL.2, July 18, 2024: 'Site visits to 8 home-based worker locations found children ages 10-14 present during working hours']"

Example of Non-Compliant Citation:

"Lahore Leather Works has child labor issues" ❌
(No source, no finding code, no date)

5.2 Verification Process

Before using AI output:

  1. Check that every claim has a source citation
  2. Click/open the source document
  3. Verify the cited text actually supports the claim
  4. Check the source date (recent audits > old audits)
  5. Assess confidence based on source count (multiple sources > single source)

5.3 Handling Unsourced Claims

If AI generates a claim without citation:

  • DO NOT USE the claim in any formal capacity
  • Flag the claim for manual research
  • Report the incident to IT/AI governance team
  • Re-query AI with more specific request for sources

6. HUMAN REVIEW CHECKPOINTS

The following decisions REQUIRE mandatory human review:

6.1 High-Stakes Decisions (Mandatory Review)

⚠️ REQUIRES MANDATORY HUMAN REVIEW:

  • Terminating supplier relationships
  • Blacklisting suppliers from approved vendor list
  • Escalating compliance issues to legal department
  • Public disclosure of supplier non-compliance
  • Contractual penalties or financial sanctions

Process: AI may provide analysis, but final decision must be made by designated manager/legal team with documented rationale.

6.2 Medium-Stakes Decisions (Recommended Review)

  • Prioritizing suppliers for urgent audits
  • Classifying findings as Critical/Major/Minor
  • Drafting corrective action plans for suppliers
  • Extending or reducing audit cycles

6.3 Low-Stakes Decisions (AI-Assisted OK)

  • Scheduling routine audits
  • Researching labor law requirements
  • Generating draft communications (with review before sending)
  • Creating training materials

7. ACCOUNTABILITY & ROLES

7.1 Responsible Parties

Role Responsibilities
Policy Owner
(Compliance Director)		 Overall policy enforcement, annual review, escalation point
AI System Administrator
(IT/Data Science)		 Configure AI systems per policy, monitor usage, technical compliance
End Users
(Procurement, Auditors)		 Follow policy, verify AI sources, flag issues
Legal/Compliance Team Review high-stakes AI decisions, legal guidance on AI use
Training Coordinator Ensure all users complete AI governance training

7.2 Enforcement

Violations of this policy may result in:

  • Retraining requirement
  • Suspension of AI system access
  • Disciplinary action per company policy
  • Escalation to Legal (if confidentiality breach)

8. INCIDENT REPORTING

8.1 Reportable Incidents

Report immediately if:

  • AI generates false or misleading information (hallucination) used in decision
  • Confidential supplier data exposed to public AI system
  • AI system exhibits bias (e.g., systematically over-/under-rating certain countries)
  • AI-assisted decision leads to supplier complaint or legal issue

8.2 Reporting Process

  1. Email: ai-governance@company.com
  2. Include: Date, AI system used, description of incident, impact
  3. Preserve: Screenshots, AI output, source documents
  4. Cooperate: With investigation and corrective action

8.3 Post-Incident Actions

  • Root cause analysis
  • System adjustments if needed
  • Additional training for involved staff
  • Update policy if gap identified

9. TRAINING REQUIREMENTS

9.1 Mandatory Training

⚠️ REQUIRED BEFORE AI ACCESS

All users of AI systems must complete:

  • AI Governance & Ethics Training (30 minutes, annually)
  • Source Verification Workshop (hands-on, 1 hour, at onboarding)
  • Refresher training if policy updated

9.2 Training Content

  • Recognizing AI hallucinations
  • Verifying source citations
  • Data privacy and confidentiality
  • Prohibited use cases
  • Human review checkpoints
  • Incident reporting

9.3 Training Verification

  • Quiz at end of training (80% pass required)
  • Certificate of completion maintained in HR records
  • No AI system access until training complete

APPENDIX: AI Source Citation Checklist

Before using AI output in any decision:

  • Every factual claim has a source citation
  • Citation includes: Document name, Supplier ID, Finding code, Date
  • I have opened the source document and verified the claim
  • The source is recent (within 18 months preferred)
  • If multiple sources exist, AI cited all relevant sources
  • Confidence level is appropriate
  • I understand the AI's reasoning (not a "black box")
  • Human review checkpoint completed if required

⚠️ IF ANY BOX UNCHECKED → DO NOT USE AI OUTPUT

QUICK REFERENCE GUIDE

✅ DO:

  • Use private/sandboxed AI systems for confidential data
  • Verify every AI claim against source documents
  • Complete mandatory training before using AI
  • Report incidents immediately
  • Follow human review checkpoints
  • Keep audit data encrypted and access-controlled

❌ DON'T:

  • Upload supplier audits to public ChatGPT/Claude
  • Make high-stakes decisions without human review
  • Use unsourced AI claims in formal reports
  • Replace physical audits with AI predictions
  • Allow AI to make autonomous supplier decisions
  • Share supplier data with untrusted AI vendors

📞 SUPPORT CONTACTS

  • AI Governance Questions: ai-governance@company.com
  • Technical Support: it-support@company.com
  • Legal/Compliance: legal@company.com
  • Training: training@company.com

This is a template policy. Organizations should customize based on their specific AI systems, risk tolerance, industry regulations, and organizational structure. Legal review recommended before implementation.

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