4 Data Governance Rules for Ethical AI in Fundraising
Guest post by Heller Consulting
Within the nonprofit sector, AI’s capabilities can be a game-changer. AI has empowered nonprofits to cut down previously time-consuming tasks, from writing personalized emails for different donor segments to determining the ideal suggested gift amount.
The benefits of AI are significant, but nonprofits should proceed with caution. Like any new development, there are nuances that should inform your organization’s approach. Setting up smart, informed data governance for your nonprofit’s AI use will ensure that your approach is sustainable and safe for your employees, donors, and community. In this article, we’ll explore four pillars for ethical governance.
Why ethical AI data governance is important
As AI tools become more accessible to nonprofits, donor data processing has changed, too. Traditional data governance practices alone fall short when protecting your organization’s, donors’, and other stakeholders’ privacy.
When you implement strong governance policies, you ensure that your entire nonprofit is elevated by AI. A few of the advantages include:
Focus on organizational resilience: Proactive frameworks prevent costly breaches and ethical missteps before they occur, building a resilient infrastructure that can adapt to new technological advancements.
Connecting technology to mission: Responsible technology use directly supports broader organizational goals and community trust, ensuring that innovation aligns with your institution's core values.
Keeping pace with the industry: Standard spreadsheets and static databases are falling by the wayside in favor of algorithms that continuously process complex supporter engagement behaviors and their giving histories. Your nonprofit needs to stay on the cutting edge to remain competitive.
Approaching tech integration through a governance-first lens transforms potential vulnerabilities into operational strengths. By securing your data pipeline early, your team can experiment with new tools confidently without risking your community's trust.
Data governance essentials for AI-powered fundraising
1. Prioritize stringent data protection
The foundation of any ethical technology strategy is an unwavering commitment to data security. Before implementing any predictive models or generative tools, your development, IT, and executive teams should collaborate to ensure existing infrastructure is protected against internal and external threats.
To build a secure environment for your constituent data, consider the following steps:
Secure sensitive constituent information against unauthorized access. Implement role-based permissions and regularly audit your systems for weaknesses, such as poor access controls and misconfigurations, weak authentication, and data corruption.
Vet the security of third-party AI tools thoroughly to guarantee they meet high standards for encryption, data sovereignty, and compliance with regional privacy regulations. If your organization has a Shadow AI problem, you’ll need a strong policy.
Collect only strictly necessary data from donors and other stakeholders. Heller Consulting’s data management guide states that “hoarded data is a liability.” By only storing necessary information in your database, you’re protecting your organization and donors from additional risk.
Ask potential software vendors to provide a detailed overview of how their algorithms store and process your specific data inputs. If a vendor cannot clearly explain their data retention and model training policies, they pose an unnecessary risk to your institution.
2. Implement policies for consistent usage
Unfortunately, user error is just as much of a risk as unvetted technology. Improper (or even inconsistent) technology use can undermine the most secure software protections. To set boundaries for acceptable data use and handling, your nonprofit should establish a unified approach.
Much of your team likely uses AI already. Your nonprofit needs a standardized set of data procedures across the board, no matter what role or AI use case a staff member is using.
As you develop standards across the board for your nonprofit’s use of AI, here are a few aspects to include in your AI use policy:
Clearly defined acceptable and unacceptable uses of AI
Approved AI tools that your organization uses
Best practices for data handling and management
Training for your team to understand how AI works and its primary use cases
Once these standards are in place, require all staff members to pass an annual AI training or attend an AI workshop. AI is rapidly evolving, so remember to review and update any training content and policies quarterly. To support your team through these changes, assign a dedicated staff member or a committee to monitor these updates and make the necessary changes.
3. Ensure transparency with stakeholders
Since fundraising is so relational, the trust you’ve built with your donors is one of your most valuable assets. Some members of your community may mistrust AI systems, so you need to openly communicate your intentions and AI use policies with donors. When they understand how you’re using AI tools to support your mission and the measures your team has implemented for safety, donors are more likely to be on board.
To foster transparency and respect donor preferences, prioritize:
Open communication. Publish your AI use policies clearly on your website and provide community members with your contact information so they can reach out for clarification if needed.
Community empowerment. Provide clear opt-out mechanisms and accessible privacy notices to respect donor and community members’ autonomy.
Consider dedicating a section in your annual report to explaining how new technologies have improved your operational efficiency. Framing data use as a stewardship and cultivation tool helps donors see the direct connection between their data and your community impact.
4. Mandate human review in automated processes
For any digital transformation projects your nonprofit engages in, it’s important to take a human-centered approach. Automation drastically reduces administrative workloads, but it shouldn’t replace your development team’s empathy and nuance.
Maintaining a human-in-the-loop approach protects your organization by:
Preventing algorithmic bias or errors: Maintain human review over AI-generated insights and content to catch algorithmic biases or factual inaccuracies before they reach the public.
Retaining empathy in outreach: While technology can optimize targeting and segmentation, the human touch ensures communications remain authentic, empathetic, and perfectly aligned with institutional values.
Use generative tools to draft the initial framework of a major donor appeal or grant proposal, but require a development officer to finalize the narrative. Ultimately, a human editor can catch AI mistakes, tonal inconsistencies, and context gaps that an algorithm cannot.
Adopting AI requires a commitment to responsible data management to truly benefit your institution. By treating data governance as an ongoing operational priority rather than a one-time IT project, your organization will be positioned to scale its impact securely.