Fundraisers don’t need more data. They need to trust the systems that use it.
Nonprofits are no strangers to trust. It’s the foundation of every donation, every recurring gift, every major donor relationship. And as artificial intelligence takes a larger role in fundraising technology, that trust is being tested in new ways.
AI has incredible potential to help teams focus, predict, and act. But without strong ethical practices, even the best technology can create unease, or worse, erode the very relationships fundraisers work so hard to build.
At Avid, we believe AI should make fundraisers more human, not less. That’s why ethical design is at the core of our platform. And it’s why this topic isn’t just technical. It’s mission-critical.
Here’s what data ethics looks like when done right, and why it matters more than ever for the future of fundraising.
The Ethical Tension at the Heart of Modern Fundraising
AI is often sold as a magic bullet.
Real-time forecasts! Smart segmentation! Automated journeys!
But for many nonprofit leaders, there’s a quiet discomfort. How much does this tool really know about our donors? What’s it doing with that knowledge? Could it accidentally cross a line?
These questions are more than hypothetical. In the recent Nonprofit Hub Podcast, Avid CTO Kevin Peters admitted it plainly: “Don’t trust AI any further than you can throw it.”
That might sound surprising coming from a tech leader. But it reflects a deeper truth: AI isn’t inherently ethical. It reflects the intentions of those who design and deploy it.
So when fundraisers worry about privacy, creepiness, or manipulation, they’re not being paranoid. They’re being responsible stewards of donor trust. And ethical AI systems should honor that.
Why AI Is (Still) Worth It
Let’s be clear: Ethical concerns shouldn’t scare nonprofits away from AI entirely. In fact, used well, AI is one of the most powerful equalizers the sector has.
It helps small shops compete at scale. It cuts through reporting noise and surfaces clarity. It automates the “dumb work” so fundraisers can focus on personal connection.
The key is how AI is used, not whether it’s used.
Bad AI guesses at patterns and spits out generic predictions. Good AI reinforces human judgment, guides action, and respects boundaries.
At Avid, we use machine learning to summarize, simplify, and elevate, not to decide, override, or exploit. Because we believe AI’s highest calling isn’t to replace fundraisers, it’s to amplify their impact
What Responsible AI Looks Like in the Nonprofit Industry
Here’s how responsible AI shows up in fundraising, and how Avid builds it in from day one.
1. It’s Transparent
If a system uses AI, it should say so. It should also make it clear what AI is doing and what it isn’t.
For example, Avid uses machine learning to summarize trends, highlight anomalies, and recommend actions. But it doesn’t make decisions on behalf of a fundraiser. You’re always in control.
That’s a core distinction between augmentation and automation. Augmentation enhances your ability to act. Automation removes you from the loop. Ethical systems prioritize the former.
2. It Respects Donor Privacy
Donor data is sacred. Any AI model that ingests, analyzes, or learns from it must do so in ways that protect individual identities.
That’s why Avid’s learning models never touch personally identifiable information. All behavioral trends are anonymized before analysis. The system doesn’t “read” donor records to predict behavior, it models macro patterns to guide micro decisions.
This ensures your team gets smarter without sacrificing trust.
3. It’s Built Around Consent
Fundraising thrives on permission-based engagement. AI tools should follow that same standard.
If a donor opts out of a communication channel, an AI shouldn’t “decide” they’re worth targeting anyway. If someone has expressed a clear preference, systems should honor it, even if that preference isn’t optimal for campaign performance.
Technology shouldn’t try to outsmart donor intent. It should reinforce it.
4. It Elevates the Human Work
Perhaps most importantly, ethical AI should free fundraisers to do what machines can’t.
As Kevin shared on the podcast, Avid was born from the desire to “automate the dumb work”; the repetitive, manual, click-and-format tasks that drain time and energy from high-value moments.
When systems do that well, fundraisers have more room to:
- Build major donor relationships.
- Craft powerful appeals.
- Coach their teams.
- Celebrate impact.
- Create space for strategy.
That’s the heart of Avid’s ethical promise: AI that puts humans front and center, not behind a dashboard, but in front of donors, where they belong.
What Happens When You Get This Right?
When AI is designed ethically, it prevents harm and creates real value.
- Donors receive messages that feel relevant, not robotic.
- Fundraisers regain time for the work that matters most.
- Small nonprofits access capabilities once reserved for enterprise orgs.
- Data becomes a trusted ally, not a source of anxiety.
Ethics Isn’t a Department. It’s a Design Principle
Ultimately, data ethics isn’t a layer you add after building a product. It’s the foundation.
That’s why Avid was designed with three non-negotiables:
- Human-first functionality: AI that supports, not replaces, fundraisers.
- Donor-respecting infrastructure: No unconsented modeling, no creepy personalization.
- Transparent logic: Insights you can explain, not just trust blindly.
We don’t believe in “growth at any cost.” We believe in growth with integrity.
Let Your Tech Match Your Values
If you’re evaluating AI tools, or wondering if your current systems are acting ethically, it’s time to ask: Does your system reflect your mission?
Do your tools:
- Help you act with clarity?
- Respect donor dignity?
- Reinforce consent and connection?
- Free up fundraisers to be more human?
You don’t need to sacrifice ethics to gain intelligence. With Avid, you can have both.
Ready to see what ethical, effective AI looks like in action? Book a demo and see how Avid helps you turn data into decisions without compromising the values that brought you to this work in the first place.
