How Nonprofits Can Adopt Transparent AI Models to Evaluate Their Programs

Program evaluation is essential for nonprofits—it helps demonstrate impact, improve services, and secure funding. AI can analyze vast amounts of data to identify patterns, predict outcomes, and assess program effectiveness. But when AI models are "black boxes" that don't explain their reasoning, nonprofits can't verify results, understand limitations, or build stakeholder trust.

Transparent AI models—also known as explainable AI (XAI)—address this challenge by providing insights into how AI reaches conclusions. This transparency is especially important for nonprofits, which need to demonstrate accountability to donors, beneficiaries, and regulators while using AI responsibly.

This guide explores how nonprofits can adopt transparent AI models for program evaluation, from understanding different types of explainability to implementing XAI tools and building stakeholder trust. For related guidance on AI governance, see our article on building an Algorithm Review Board.

Different AI models provide different levels and types of explainability. Understanding these options helps you choose the right tools for your evaluation needs:

Inherently Explainable Models

Some AI models are naturally transparent because they use simple, interpretable structures:

• Linear models: Simple regression models that show how each input variable contributes to outcomes
• Decision trees: Models that show clear if-then logic paths, making decisions easy to trace
• Rule-based systems: Systems that use explicit rules that can be reviewed and understood
• Generalized additive models (GAMs): Models that show how each variable affects outcomes independently

These models are often less powerful than complex "black box" models but provide clear explanations that stakeholders can understand and verify.

Post-Hoc Explanation Methods

For complex models like neural networks, you can use techniques that explain decisions after the fact:

• Feature importance: Shows which input variables most influenced a decision
• SHAP values: Quantifies how much each feature contributed to a specific prediction
• LIME: Creates local explanations for individual predictions by approximating the model
• Attention mechanisms: Highlights which parts of input data the model focused on
• Counterfactual explanations: Shows what would need to change to get a different outcome

Hybrid Approaches

Some approaches combine explainable and powerful models, using simple models to approximate complex ones or using explainable models for critical decisions while using complex models for other tasks.

Here's how to adopt transparent AI models for program evaluation:

1. Choose the Right Model Type

Start with inherently explainable models when possible. They're easier to understand and verify, which builds stakeholder trust. Use complex models with post-hoc explanations only when you need additional predictive power.

• Use decision trees or linear models for straightforward evaluations
• Consider rule-based systems for evaluations with clear criteria
• Use post-hoc explanation methods for complex models when needed
• Test model accuracy and explainability together—don't sacrifice one for the other

2. Ensure Data Quality

Transparent AI is only as good as the data it uses. Ensure your evaluation data is accurate, complete, and representative:

• Clean and validate data before using it in models
• Address missing data appropriately
• Ensure data represents the populations you serve
• Document data sources and collection methods

For more on data preparation, see our article on building a data-first nonprofit.

3. Validate and Test Models

Test transparent AI models to ensure they're accurate, fair, and explainable:

• Validate model accuracy using holdout data
• Test for bias across different demographic groups
• Verify that explanations are accurate and understandable
• Compare model predictions to expert judgment
• Test edge cases and unusual scenarios

4. Communicate Results Clearly

Make AI explanations accessible to stakeholders who may not have technical expertise:

• Use plain language to explain AI decisions
• Create visualizations that show how factors contribute to outcomes
• Provide examples that illustrate model reasoning
• Document limitations and uncertainties
• Make explanations available in multiple formats (written, visual, verbal)

5. Build Stakeholder Trust

Transparency alone isn't enough—you need to build trust through consistent, accurate, and fair use of AI:

• Involve stakeholders in model development and validation
• Share explanations proactively, not just when asked
• Admit when models make mistakes and explain how you'll improve
• Create feedback mechanisms for stakeholders to question AI decisions
• Regularly audit models for accuracy and fairness

Building Trust Through Transparency

Transparent AI models are essential for responsible program evaluation in nonprofits. By using explainable AI, you can understand how evaluation results are reached, build stakeholder trust, ensure accountability, and improve programs based on clear insights.

Start with simple, inherently explainable models, ensure data quality, validate results, and communicate clearly. Build trust through consistent, fair, and transparent use of AI. With the right approach, transparent AI can transform program evaluation while maintaining the accountability and trust essential to nonprofit success.

For more on AI governance, see our article on building an Algorithm Review Board. For guidance on creating AI policies, see our article on AI policy templates for nonprofits.