Mitigating biases in prospect research

Worried that your prospect pipeline is not fully reflective of the community that your organization serves? We have tips to help identify, acknowledge, and mitigate some common biases in Hilborn Charity eNews. 

It is a truth universally acknowledged in fundraising that a major giving portfolio requires prospect identification. We may not like to acknowledge it, mind—prospect identification often requires a lot of effort for results that may not materialize for years. Like it or lump it, prospect identification is a fundamental element of fundraising, and historically has been rife with bias. 

We tend to talk to people we already know, and look for more people who look like the people we already know. Another truth in today’s fundraising world, however, is that our beneficiaries, donors, and constituents are a beautiful mosaic of identities, and our fundraising strategies and approaches need to reflect this for long-term sustainability. 

We wrote about this topic in our book, From the Ground Up: Prospect Research for Nonprofits, and spoke about it at the NTC Gathering in March 2026. We are using the learnings from those experiences to share our thoughts and practices in this article. We don’t have all the answers—or even all of the questions—and would like you to treat this article as an invitation for discussion and dialogue, rather than facts set in stone.

Three common biases

Let’s take a look at three common biases we encounter in prospect identification and some mitigation strategies for each of them. But, before we dive in, let’s acknowledge the elephant in the room; that’s right, AI. Specifically, the Large Language Models (LLMs) such as Claude, ChatGPT and CoPilot that are increasingly ubiquitous in the workplace. We could write at length about this technology but here are a few key points:

• AI tools are neither magical nor a replacement for the important work that you bring to your work, 
• the technology has been trained on unimaginably vast quantities of data with limited (if any) regard for quality, this means that all the biases and falsehoods that live within the vast reams of information are baked right into this eager little robot, so 
• you can be the cleverest prompt writer and still find yourself with results that are riddled with problems.

Remember that critical thinking is your superpower; please use it lavishly when you are either using an AI tool yourself, or when you are presented with the results that impressed someone else. 

A human should always be in the loop. You don’t want to find yourself presenting a fantastic new funding partner who aligns “perfectly” with your mission, (backed by sources) only to find that the sources and your prospect is completely fabricated. 

OK? Let’s get on with it.

Here is a decidedly non-exhaustive list of biases you are likely to encounter in your prospecting activities.

Unconscious bias

All of us have unconscious biases; mental shortcuts that lead to snap judgments about people’s talents or character based on anything from race to gender to ability. When we work in fundraising and in prospect identification, we may have to confront biases within ourselves regarding our perceptions of the way a wealthy, philanthropic individual behaves or looks. 

The mitigation: Pause and ask why a potential partner is dismissed out of hand. Is it because they are truly not a fit for your organization? Or, is it because they don’t fit into the existing narrative of what a major gift prospect looks like? Fundraising is often driven by peer networking and marketing to known audiences, which can lead to self-fulfilling prophecies about who the best prospects are. 

Sometimes, true gems can be overlooked simply because they represent unknown territory, which can be intimidating and scary. Working within the time, technology, and financial constraints you are facing, try to employ a variety of prospecting techniques in order to improve your outcomes and catch things you would otherwise have missed. 

Taking a pause, planning your research approach, and opening up your strategies for discussion can make a huge difference. 

Confirmation bias

Confirmation bias pushes us to favour evidence that supports our point of view while ignoring evidence that challenges it. This bias is especially pervasive and difficult to face, as it’s comfortable to be in a bubble of rightness and only take in information that reinforces that bubble. 

Confirmation bias is also deeply hardwired in our brains. It helps us deal with the vast amounts of information that we must absorb and process each day. It’s quicker and easier to react to information that makes sense from our own experience, rather than making the effort to view incoming information in all its complexity. The damage in fundraising happens when people choose to believe in a particular narrative because it suits their preferences. This can be particularly pernicious in predictive modelling and wealth screenings, which start from a set of data points that are biased towards specific indicators of wealth and status which are deeply rooted in systemic discrimination.

The mitigation: We are not saying that you cannot do predictive modelling or wealth screening, instead, acknowledge how your parameters affect your results. There is nothing wrong with relying on tried-and-true methods to sift through your data, but it’s important to understand how the data points being used can potentially create an echo chamber in your pipeline, rather than yielding new insights.

Selection bias

This refers to targeting a group of individuals or companies for an activity, (an event, committee, or special project) based on incomplete or faulty criteria. If your organization has historically hosted events or received gifts from specific demographics, then your database analysis may lead you to believe that individuals from certain demographics are more inclined towards your cause or to give—leading us right back to confirmation bias. 

The mitigation: To break free of selection bias, you must actively seek out underrepresented groups, communities, and individuals, and be ready to critically reflect on who is present and who is absent from your lists, and why. Where possible, invite input from colleagues who can critically assess and make recommendations for improvement. This work takes time and should not be left to the last minute.

It will be difficult—if not impossible—to rid your work of all bias, and it may be a topic of conversation that your workplace isn’t ready for. This is not a one-and-done topic of conversation; critically thinking about biases and their effects on fundraising is a journey that requires learning and humility. However, examining biases within your research, your organization, and in your resources, remains a vital step in moving away from the status quo towards a more thoughtful and nuanced approach.