Explainable AI in Grantmaking: Why Transparency Builds Trust
When a foundation declines a grant application, the applicant usually wants to know why. For decades, the answer came from a program officer who could explain, at least roughly, what the review committee weighed. As more funders bring AI into screening, scoring and prioritization, that explanation becomes harder to give. A model may flag an application as “low fit” without anyone in the building being able to say what drove the result.
That is the core problem explainable AI tries to solve. It is not a single technology but a set of methods and practices that make automated recommendations understandable to the people affected by them. In grantmaking, where decisions shape which communities receive resources, understanding is not a technical nicety. It is part of the funder’s accountability.
This article covers what explainability means in practice for foundations, which methods are realistic, and how transparency strengthens relationships with applicants, grantees, boards and the public.
Why Black-Box Decisions Do Not Fit Philanthropy
Private funders are not bound by the same rules as public agencies, but they operate on trust. Grantees share sensitive information, communities accept the foundation’s presence, and the public grants tax advantages on the understanding that the money serves the common good.
Power is already unequal
The relationship between a funder and an applicant is lopsided. Applicants rarely feel able to challenge a decision. When the reasoning is hidden inside a model, that imbalance grows: the applicant cannot respond, and staff cannot defend or correct the outcome.
Errors compound quietly
An opaque model can disadvantage the same kinds of organizations cycle after cycle without anyone noticing. Those patterns are described in detail in the discussion of AI bias in philanthropy and who gets left behind. Explainability is one of the main ways to catch them early, because you cannot fix a pattern you cannot see.
What “Explainable” Actually Means
Explainability works at different levels, and each answers a different question for a different audience.
| Level | Question it answers | Main audience |
|---|---|---|
| Global explanation | Which factors does the model generally rely on? | Board, leadership, evaluators |
| Local explanation | Why did this application get this score? | Program officers, applicants |
| Process transparency | Where in the workflow is AI used, and who decides? | Applicants, grantees, public |
| Data transparency | What information was the model trained or run on? | Auditors, partners, regulators |
Simple models are often the best explanation
For many grantmaking tasks, a transparent scoring rubric or a simple, interpretable model performs about as well as a complex one and is far easier to explain. Before reaching for a sophisticated system, ask whether a checklist that staff already understand would do the job. Complexity should be justified by a real gain, not by novelty.
Explanations for complex models
When a more complex model is used, techniques exist to estimate which inputs influenced a particular result. These are useful, but they are approximations. They show what correlated with a score, not necessarily the true reason, and they need a skilled person to interpret them. Treat them as a starting point for review, not as a final answer to give an applicant.
Designing a Transparent AI Workflow
Explainability is as much about process as about algorithms. A foundation can make AI use understandable even when the underlying model is complicated.
Map where AI touches decisions
Write down each step of the grant cycle and mark where AI is involved: sorting incoming applications, checking eligibility, summarizing proposals, suggesting priorities. For each step, note who reviews the output and who makes the final call. This map is often revealing, because tools tend to spread into decisions nobody formally approved.
Keep AI advisory
A common and sensible pattern is to let AI prepare and organize information while people make decisions. Imagine an environmental fund that uses AI to summarize applications and check them against eligibility criteria, while every funding recommendation is written by a program officer who has read the full proposal. Applicants can then be told, truthfully, that a person made the decision and why.
Record reasons in plain language
Whatever the model produces, the decision record should include a short human-written explanation. If staff cannot write that explanation, the decision is not ready to be made.
If you cannot explain a funding decision to the organization it affects, AI has not made the decision better. It has only made it harder to question.
Communicating with Applicants and Grantees
Transparency only builds trust if it reaches the people outside the foundation.
- State on your website and in application guidelines whether and how AI is used in review.
- Explain what kinds of information the tools look at and what they do not consider.
- Offer a way to ask questions about a decision and to correct factual errors in an application record.
- Tell grantees if their reports are analyzed with AI and how the results are used.
Disclosure without jargon
A paragraph in clear language is more useful than a technical appendix. Applicants want to know three things: is a person making the decision, what was considered, and can they respond. Answer those directly.
Consistency matters too. If the guidelines say AI only summarizes proposals, staff should not start using it to rank them without updating the disclosure. Review the wording each grant cycle, alongside the workflow map, so that what applicants read matches what actually happens.
Questions Boards and Staff Should Ask Vendors
Many foundations use AI through grants management software rather than building their own models. Explainability then depends on what the vendor provides.
- Can the system show which factors influenced each recommendation or score?
- Can we switch off automated scoring and use only summarization or sorting features?
- Has the model been tested for uneven results across organization size, geography or community served?
- What data was the model built on, and does it include other clients’ applicants?
- Will we be notified when the model is updated, and can we compare results before and after?
If a vendor cannot answer these questions, that is useful information in itself.
Transparency as a Long-Term Investment in Trust
Explainable AI can feel like extra work: more documentation, simpler models, longer conversations with applicants. But the cost of opacity is higher. A single unexplained rejection that turns out to be an error, or a pattern of exclusion discovered by an outside group, can damage a foundation’s reputation with the very communities it wants to serve.
Funders that are open about how they use AI gain something valuable in return. Applicants trust the process even when the answer is no. Grantees share more honest information. Boards can oversee the tools with confidence. And staff keep the judgment and relationships that make grantmaking more than a sorting exercise. Transparency does not slow good philanthropy down; it keeps AI working in service of the mission rather than quietly redirecting it.
