US Nonprofit Management

AI Risk Management Policy Generator for US Nonprofits

Establish ethical, legal, and operational safeguards for AI adoption in your nonprofit organization.

#nonprofit#governance#risk-management#ai policy
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Created by PromptLib Team
Published February 12, 2026
4,823 copies
4.5 rating
Act as an expert in Nonprofit Law and AI Governance. Your task is to draft a comprehensive 'AI Risk Management Policy' for [ORGANIZATION_NAME], a US-based nonprofit focused on [MISSION_AREA]. 

Your policy must adhere to the NIST AI Risk Management Framework and consider the following specific constraints: [SPECIFIC_CONSTRAINTS].

Please structure the policy with these sections:
1. Purpose and Scope: Define how AI fits into the mission and which staff/volunteers this applies to.
2. Ethical Principles: Outline commitments to transparency, fairness, and human oversight.
3. Risk Assessment Tiers: Categorize AI tools by risk (e.g., Low-risk administrative vs. High-risk beneficiary data analysis).
4. Data Privacy and Security: Specific protocols for handling donor information and PII under US laws.
5. Procurement and Vendor Review: Criteria for selecting third-party AI tools.
6. Use Cases and Prohibitions: List approved uses (e.g., grant writing assistance) and prohibited uses (e.g., autonomous decision-making on aid eligibility).
7. Governance Structure: Define who is responsible for oversight (e.g., an AI Committee or the Executive Director).
8. Incident Response: Procedures for addressing AI hallucinations, bias reports, or data breaches.

Use a professional, authoritative, yet accessible tone suitable for a Board of Directors review. Ensure the policy balances innovation with the 'Do No Harm' principle central to nonprofit work.
Best Use Cases
Drafting an internal handbook for staff using Generative AI.
Preparing a formal AI governance document for Board approval.
Setting guidelines for external consultants using AI on behalf of the nonprofit.
Ensuring compliance with donor requirements regarding data protection.
Establishing a framework for conducting 'Bias Audits' on automated systems.
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