What Is Responsible AI? A Guide for Nonprofit Leaders
AI is transforming the nonprofit sector, helping organizations work smarter, scale faster, and deepen relationships with donors and communities.
But 2025 research from the Blackbaud Institute reveals a striking paradox. While 80% of fundraisers use AI tools daily, 96% still have concerns, especially around misinformation and data privacy. And only 14% of organizations have a formal AI policy in place.
This gap highlights an urgent need for responsible AI in the nonprofit sector.
What is responsible AI? Responsible AI is the practice of designing, developing, and deploying artificial intelligence systems in a way that prioritizes ethical standards, transparency, and accountability.
Throughout this guide, we’ll define responsible AI principles and highlight practical steps and considerations to help you apply these principles in your organization’s daily operations.
Why Responsible AI Matters for Nonprofits
Organizations rely on the confidence and goodwill of donors, volunteers, and communities to fulfill their mission, making trust essential for success.
As you explore new ways to use AI, it’s important to ensure that technology is trustworthy, supports your organization’s mission, and protects the privacy of everyone involved.
Responsible AI provides a path forward, helping you and your team innovate with confidence while safeguarding sensitive data and strengthening the relationships that matter most.
Key Principles of Responsible AI
As responsible AI evolves, best practices are shaped by respected frameworks, such as those from the EU Ethics Guidelines for Trustworthy AI, OECD, NIST AI Risk Management, and by the collective insights of sector experts and advisory councils. These sources consistently highlight the importance of ethical, transparent, and accountable AI practices, which are especially important for nonprofits.
Drawing from these widely recognized standards, the following six principles offer a practical foundation for nonprofits looking to put responsible AI into action for their unique mission and strengthen trust with their stakeholders.
Fairness
Fairness ensures that AI systems serve all communities equitably. This means actively working to prevent bias and making sure technology reflects the diversity of the people you serve.
How this principle can be applied:
- Use diverse, representative data when training AI models.
- Regularly audit algorithms for bias and unintended consequences.
- Ask critical questions about who benefits from AI and who might be left out.
- Involve stakeholders from different backgrounds in design and testing AI.
Transparency
Transparency builds trust by making AI systems understandable. Nonprofits need to ensure that staff, donors, and communities know how AI works and why it makes certain recommendations.
How this principle can be applied:
- Choose AI solutions that offer clear explanations for their outputs.
- Communicate openly about how AI is used in your organization.
- Provide training so users can interpret and validate AI-driven insights.
- Invite questions and feedback from stakeholders.
Accountability
Accountability means having clear governance and oversight for AI. Ensure that ethical standards are upheld and that there are processes in place to address concerns or mistakes.
How this principle can be applied:
- Establish formal oversight structures, such as AI councils or committees.
- Document policies and procedures for AI use.
- Engage stakeholders in ongoing review and improvement.
- Monitor AI systems continuously and act on feedback.
Data Privacy and Security
Data privacy and security are foundational to donor trust and organizational reputation. It’s critical to protect sensitive information and use data ethically.
How this principle can be applied:
- Collect, store, and use data in compliance with privacy laws and ethical standards.
- Limit access to sensitive information and use encryption where possible.
- Be transparent with donors and stakeholders about how their data is used.
- Regularly review and update data protection policies.
Inclusiveness
Inclusiveness ensures that AI technology reflects the diversity of the communities it serves. This means designing solutions that are accessible and relevant to everyone.
How this principle can be applied:
- Use representative data that reflects all segments of your community.
- Involve diverse voices in the design, development, and testing of AI solutions.
- Ensure user experiences are accessible to people with different abilities and backgrounds.
- Continuously seek feedback to improve inclusivity.
Human Oversight
AI systems should be designed to support and enhance human decision-making, not replace it. Human oversight ensures that technology remains aligned with organizational values, adapts to changing needs, and maintains accountability.
How this principle can be applied:
- Thoroughly validate AI models for accuracy and reliability.
- Ensure the data informing your AI systems is accurate, up-to-date, and representative of your stakeholders.
- Integrate human review into decision-making workflows.
- Closely monitor AI performance and adapt as needed.
- Establish clear escalation paths for concerns or exceptions.
Responsible AI vs. Ethical AI: What’s the Difference?
Understanding the distinction between ethical AI and responsible AI empowers organizations to move from intention to action.
Ethical AI defines what is right, while responsible AI ensures those values are consistently put into practice and are woven into daily decisions, policies, and outcomes.
| Ethical AI | Responsible AI | |
| Core Idea | Sets moral principles and values | Applies principles in real-world use |
| Purpose | Guides what AI should strive for | Ensures AI delivers on values |
| Approach | Aspirational, often theoretical | Practical, focused on implementation |
| Governance | Encourages reflection and discussion | Requires oversight, policies, and accountability |
| Measurement | Difficult to measure | Directly affects outcomes and user experience |
| Impact | Shapes development philosophy | Directly affects outcomes and user experience |
| Relevance | Broad, applies to all industries | Tailored to organizational mission and context |
Challenges in Implementing Responsible AI
Despite growing recognition of its importance, many organizations face barriers to responsible AI adoption. Recent research from the Blackbaud Institute highlights several key obstacles, among others, including:
- Legal compliance challenges: The constantly evolving landscape of AI regulations and data privacy laws can make adoption feel overwhelming.
- Lack of formal AI policies: Only 14% of organizations have a documented AI policy.
- Resource constraints: Less than one-third of fundraisers feel equipped to explore AI in their organizations.
- Data quality issues: Poor data health can undermine AI effectiveness and fairness.
These challenges often arise from uncertainty about where to start, limited capacity, or concerns about risk. But every challenge is an invitation to lead.
By investing in responsible AI, organizations build trust and position themselves for sustainable growth. The journey may be complex, but every step forward strengthens your mission and your impact.
How to Get Started with Responsible AI
You can start with these four building blocks as a foundation.
- Governance: Create councils and processes that align AI with your values and standards. Effective governance ensures that AI initiatives are mission-aligned and meet legal, security, and ethical standards.
- Policy: Develop clear, actionable policies that balance innovation with risk management. Policies should be accessible and relevant to everyone in the organization, not just technical teams.
- Empowerment: Provide training, support, and governance so teams can innovate responsibly. Empowerment means equipping staff with the skills and confidence to use AI ethically.
- Process: Democratize data skills through shared frameworks and literacy programs. Processes should encourage curiosity, foster collaboration, and make responsible AI accessible to all.
Every step you take strengthens your organization’s foundation for responsible AI and helps build lasting trust with your stakeholders.
Leading with Trust: Blackbaud’s Commitment to Responsible AI for Social Impact
Nonprofits have the power to shape technology that truly serves people and communities. By embracing responsible AI, you can innovate with confidence, protect what matters most, and set a new standard for trust in your sector.
Blackbaud stands at the forefront of this movement.
As part of our Intelligence for Good® committment, every solution we deliver is governed by fairness, transparency, human oversight, privacy, and mission alignment.
We leverage the world’s largest philanthropic database, design tools for explainability, and prioritize human decision-making at every step. Data stewardship is central to our promise—honoring the trust placed in us by thousands of organizations and donors worldwide.
But responsible AI isn’t just about our technology—it’s about empowering you. We’re committed to supporting your journey, helping you build the confidence, skills, and knowledge to use AI wisely and responsibly.
Ready to take the next step? Join the waitlist for our free AI certification course. The first module will launch in early 2026.
