Using AI Responsibly in Donor Research: A Quick Guide
Running a nonprofit involves managing a large amount of supporter data. When your community members trust you with sensitive data, from their personal addresses to their financial information, you have a duty to make sure it remains secure. In the new age of artificial intelligence (AI), data privacy remains more important than ever for building trusting relationships with your supporters.
However, AI is a valuable tool for the nonprofit sector. Automated systems reduce administrative time and expand your ability to analyze engagement habits, giving your team more capacity to cultivate relationships with donors and community members alike. This article explores how you can use AI to improve your organization’s donor research processes without compromising data security.
1. Conduct security audits for predictive models
Nonprofit software vendors are increasingly including AI models within their fundraising systems. When selecting software providers, it’s important to review their data policies to make sure they comply with industry privacy standards.
360MatchPro recommends using a uniform set of data standards when exploring AI solutions. Data governance policies establish privacy rules for your organization, including who can see what information, what to do in the case of a data breach, and best practices for maintaining security. When applying these policies to AI models, make sure that the software you choose restricts its use of your data to within your organization. This means that your data doesn’t leave your servers when using automated systems.
For example, you may use a predictive AI model to assess your donors’ major giving potential. However, you should create parameters to make sure the AI works only with the provided data from your donor database and prospect research platform and does not share the information it receives with outside servers.
2. Prioritize data anonymization in broad-scale analysis
One of the biggest benefits of AI for nonprofits is its ability to assess and synthesize insights from large amounts of data without much administrative labor. Many nonprofits use this ability to reduce the administrative time required to identify potential major donors, run renewal campaigns, and cultivate donations. However, feeding large datasets into AI engines that allow public access exposes your organization to risk.
Before using AI to read your data, strip any personally identifiable information (PII) from your data set. Typical PII includes full names, contact details, financial information such as credit card numbers, and social security numbers. If you don’t give AI access to sensitive personal information, you can still guide your fundraising strategy using large-scale automated data analysis without the risk of a data breach.
You should also anonymize any data you use to train organization-specific AI models. While a custom AI program is designed to exactly fit your organization’s needs, using synthetic or anonymized data to train your system protects your real donors’ privacy while you develop new workflows.
3. Establish access controls for donor profiles
According to DonorSearch by EverTrue, a donor profile is a centralized record created within a nonprofit’s database or constituent relationship management (CRM) system that stores all of the important information gathered about a current or prospective donor. Nonprofits often use AI to analyze this data across their donor base to gather behavioral insights about giving trends, popular engagement types, and initiatives that matter deeply to their constituents. Your team can then use these insights to create programming and outreach initiatives that reflect donors’ interests, driving up retention.
While donor profiles are a useful tool for building an understanding of your donor base, storing a large amount of sensitive information about your donors in one place leaves you vulnerable to leaks. The first step in responsible data governance is making sure only authorized personnel have access to these detailed profiles. Enable two-factor authentication and identity checks in your login procedures, and use access controls to make sure only team members who work directly with donors can view your profiles and conduct AI-powered analyses of them.
4. Maintain human oversight
Successful donor relationships are built on human connection. While automation can provide an incredible amount of administrative assistance in your daily workflows, it’s important to remember that your donors care about your organization because of the community you’ve built and the cause you work toward together. Have your AI workflows manage the data-centric elements of the prospect research process, and use the additional time your administrative team gains to foster relationships within your donor base.
Another important thing to consider when building a balance between AI and human-led workflows in your donor research process is AI bias. Since AI operates off of the models it’s trained on, its assessments sometimes lack nuance or inadvertently provide incorrect information due to input data that isn’t inclusive. Your team should closely monitor your AI outputs to make sure they remain accurate and representative of your supporter base.
Assign a team member to spot-check your AI workflows, automation systems, and data outputs for inaccuracies or hallucinations. This recurring audit will help your team flag any areas of your AI workflows that need tweaking or that are producing inaccurate results. Regular AI reviews also minimize your risk of sharing private data with the public.
Publicly prioritizing data privacy and governance policies builds community trust. Make sure to publish an updated summary of your AI use and data privacy standards on your website so your supporters know that their data is protected within your systems. Innovative use of modern systems, grounded in data privacy, allows your organization to use AI to improve your donor research and fundraising processes while maintaining strong relationships with your donors.
