Ethical AI: Principles in Action
AI introduces complex challenges—but ethical principles provide a way forward. Explore key dilemmas and the principles that address them.
AI in Social Services
AI-driven eligibility systems can increase efficiency—but what happens when they get it wrong?
AI & Fundraising Equity
AI optimizes fundraising—but does it risk favoring large donors at the expense of small ones?
AI in Hiring & HR
AI helps streamline hiring—but can it unintentionally introduce bias?
AI & Impact Measurement
AI helps measure nonprofit impact—but does it prioritize numbers over human stories?
Ethical AI in Nonprofits: A Framework for Responsible Innovation
AI has the power to amplify nonprofit impact—but its responsible use is essential. Ethical AI is about more than compliance; it’s about ensuring technology aligns with your mission, values, and the people you serve.
Why Ethical AI Matters
Nonprofits drive social good, but AI—if unchecked—can introduce bias, automate decisions without oversight, or prioritize efficiency over equity. Establishing strong AI principles keeps organizations mission-driven, even as technology evolves.
Five Core Principles for AI Ethics
Transparency & Accountability
AI systems should be explainable. Clearly label AI-generated content and disclose how AI influences decision-making.
Data Privacy & Security
Ethical AI requires strong data protection policies. Define clear guidelines on what data AI can access and how it’s stored.
Human Oversight in Critical Decisions
AI can assist, but humans must remain in control—especially in areas impacting people’s lives, finances, or rights.
Bias Monitoring & Fairness
Regularly audit AI outputs to prevent bias from creeping into decision-making. Equity must be an ongoing priority.
Governance & Ethical Leadership
Assign an AI Ethics Steward or create an oversight committee to ensure alignment with nonprofit values and accountability.
Turning Principles Into Action
Ethics are only meaningful when applied. Implementing these principles requires:
- Establishing AI policies to guide responsible adoption.
- Training teams on AI ethics and best practices.
- Engaging stakeholders in conversations about AI’s impact.
- Conducting regular audits to ensure fairness and accountability.
AI is already shaping how nonprofits work. But have we defined what’s ethical?
AI is already changing nonprofit work. From automating donor outreach to optimizing impact reporting, nonprofits are seeing real gains. But with every AI-driven improvement comes a question: Is this ethical?
If organizations don’t define their AI principles, those principles will be defined for them—often by accident. The challenge isn’t whether AI should be used, but whether its use aligns with your mission.
AI in Hiring
Before AI:
HR manually reviews each application, ensuring diversity considerations.
After AI:
AI ranks applicants, reducing hiring time by 30%.
✅ Efficiency Win:
AI streamlined resume screening, saving 15+ hours per week.
AI in Fundraising
Before AI:
Fundraisers reach out manually, building relationships over time.
After AI:
AI prioritizes high-value donors using predictive analytics.
✅ Efficiency Win:
Increased major gifts by 20% through AI-driven segmentation.
AI in Social Services
Before AI:
Caseworkers determine eligibility based on interviews & documents.
After AI:
AI automates eligibility checks, reducing wait times.
✅ Efficiency Win:
Reduced processing time by 50%, increasing efficiency.
AI in Impact Measurement
Before AI:
Reports manually written based on qualitative & quantitative data.
After AI:
AI generates reports, summarizing key insights automatically.
✅ Efficiency Win:
Faster impact reporting with consistent formatting.
What ethical dilemmas has your organization encountered?
Share an AI challenge you've faced in your nonprofit work.
The Path Forward: From Dilemmas to Principles
AI ethics isn’t just about identifying problems—it’s about defining how we solve them. This structured framework helps organizations move from recognizing AI dilemmas to drafting meaningful AI principles.
Step 1: Observe & Log Ethical Dilemmas
When AI creates ethical tensions—whether around bias, transparency, or unintended harm—document them. Don’t rely on memory. Create a shared record that your team can return to over time.
Step 2: Define Decision Criteria
Organizations need clear criteria to guide AI-related decisions. Not every dilemma requires the same response—but every dilemma needs a structured way to think through it.
Step 3: Draft Principles & Guardrails
Translate dilemmas into clear, actionable AI principles. Think of AI principles as a code of conduct for responsible AI use. Each principle should include practical guardrails.
Example Dilemma:
A nonprofit uses AI to screen potential beneficiaries. The AI flags some applications as 'low-priority' due to missing data, disproportionately affecting marginalized communities.
Reflection Question:
Are we reinforcing existing inequities by automating decision-making?
Define Your AI Principles
Which values should guide your AI-related decisions?
Interactive AI Use Cases & Ethical Dilemmas
AI adoption offers tangible benefits, but also raises ethical trade-offs. Select a use case to explore its key dilemmas.
Hover over a use case to see its ethical dilemmas.
The 5-Point AI Principles Framework
Your organization doesn’t need to reinvent AI ethics—it needs clear internal principles.
Commit to Transparent AI Use
Decide where AI-generated content will be labeled. Be upfront about AI’s role in decision-making.
This Conversation Needs Your Voice
AI ethics isn’t a solo effort—it’s a shared responsibility. This is a peer draft—an evolving space for refining the conversation on AI principles. Your dilemmas, questions, and perspectives will shape the final version of this resource.
What dilemmas have you encountered?
Share an AI challenge you've faced in your nonprofit work.
Help Shape This Page
What questions should nonprofits be asking?
Recent Submissions
- ✔How do we ensure AI-generated donor outreach is truly personalized?
- ✔What happens when an AI-driven hiring tool favors one demographic over another?
- ✔Should nonprofits disclose when AI is used in service eligibility decisions?
The Three-Stage Process for Defining AI Principles
AI dilemmas aren't just theoretical—they emerge when AI is actively used in real-world applications. This process helps you move from recognizing dilemmas to structuring a response.
Stage 1: Recognize Ethical Dilemmas
Ethical dilemmas aren’t hypothetical—they emerge when we start using AI in real ways. As you experiment with AI, it's important to recognize where ethical questions arise.
Stage 2: Apply Decision-Making Criteria
Not all dilemmas are created equal. We help you structure a decision-making framework to determine where AI aligns with your values—and where it doesn’t.
Example Criteria:
- Does this AI application increase or decrease equity?
- Does this AI tool replace human judgment, or does it assist human decision-making?
- Does this use of AI require transparency or disclosure?
Stage 3: Formalize AI Principles for Your Organization
AI principles are a living set of guidelines for how you will and won’t use AI. These principles should be reviewed and updated as your AI usage expands.
The Decision-Making Framework: How Do We Decide What’s Ethical?
Organizations need clear criteria to navigate ethical AI dilemmas. This 5-factor model helps structure ethical decision-making in nonprofits.
Mission Alignment
Does this AI use case further the organization’s core mission?
Example:
AI in donor engagement vs. AI for intrusive data tracking.
Transparency & Disclosure
Does this AI application require disclosure to stakeholders?
Example:
AI-generated grant proposals → Should funders be informed?
Equity & Inclusion
Does this AI application reinforce bias or create inequities?
Example:
AI-driven hiring tools → Are they screening out marginalized applicants?
Human Oversight & Accountability
Can a human intervene or override AI decisions?
Example:
AI-assisted client eligibility → Do humans still have the final say?
Data Privacy & Security
Does this AI system protect sensitive data appropriately?
Example:
AI for donor segmentation → Are donor details stored securely?