Mapping AI Tools to Evaluation Questions – UN AI Resource Hub
PRACTICAL MAPPING The UN AI Resource Hub surfaces a wide range of AI tools already used across the UN system. For evaluators, the key task is not to assess the technology itself, but to evaluate how these tools shape decisions, evidence, and outcomes.
Mapping AI Tools to Evaluation Questions
How EvalCommunity Users Can Evaluate AI in Practice
The UN AI Resource Hub
Surfaces AI tools used across the UN system
The Evaluator's Task
Evaluate how AI tools shape decisions & outcomes
This practical mapping connects common AI tool categories from the UN AI Resource Hub with the evaluation questions they raise. The goal is not to assess the technology itself, but to evaluate how these tools shape decisions, evidence, and outcomes in practice.
This mapping helps evaluators move beyond technical performance metrics to center ethics, equity, and learning in AI assessment.
1. Natural Language Processing (NLP) Tools
Typical Uses
📝 Analyzing open-ended survey responses
💬 Processing beneficiary feedback & complaints
📄 Reviewing policy documents & reports
🤖 Chat-based systems for data collection
Core Evaluation Questions
Relevance & Usefulness
- What evaluation need is this NLP tool addressing?
- Does automated text analysis answer questions that matter to decision-makers?
Validity & Bias
- Which languages or dialects are underrepresented?
- How does the model handle culturally specific expressions?
Transparency & Explainability
- Can evaluators explain how themes were generated?
- Are decision-makers aware of automated coding limitations?
Comparability & Learning
- Are AI-generated themes consistent with human analysis?
- How are discrepancies between AI and human analysis handled?
2. Machine Learning & Predictive Models
Typical Uses
⚠️ Predicting food insecurity or displacement risks
🎯 Identifying populations for targeting
📈 Forecasting program demand or outcomes
🏷️ Risk scoring & early warning systems
Core Evaluation Questions
Effectiveness
- How accurate are predictions vs. observed outcomes?
- Do predictive models improve decision timing or quality?
Equity & Fairness
- Which groups are more likely to be flagged—or overlooked?
- Are historical biases being reinforced through training data?
Accountability
- Who is responsible when model outputs influence harmful decisions?
- Is there a process for contesting or overriding predictions?
Contribution & Attribution
- Did improved outcomes result from AI or complementary human action?
- Would similar results have occurred without the model?
3. Computer Vision & Image Analysis
Typical Uses
🌍 Climate and land-use monitoring
🏚️ Infrastructure damage assessments
📡 Remote monitoring in inaccessible areas
🛰️ Satellite imagery analysis
Core Evaluation Questions
Accuracy & Reliability
- How often are AI classifications validated against ground truth?
- Under what conditions does accuracy degrade?
Coverage & Inclusion
- Which areas are invisible due to data gaps?
- Does reliance on remote sensing reduce community engagement?
Cost-Effectiveness
- Does computer vision reduce costs without compromising quality?
- What trade-offs exist between speed and contextual accuracy?
Use in Decision-Making
- How are image-based findings translated into policy decisions?
- Who interprets the outputs—and with what training?
4. Decision-Support & Recommendation Systems
Typical Uses
🏥 Health or social service prioritization
📚 Education system planning
📊 Resource allocation & workload management
⚖️ Triage tools & case prioritization
Core Evaluation Questions
Decision Influence
- To what extent do users follow, override, or ignore AI recommendations?
- How does AI reshape professional judgment rather than replace it?
Trust & Adoption
- Which users trust the system—and which do not?
- How does trust differ by role, experience, or context?
Unintended Effects
- Does reliance narrow the range of considered options?
- Are edge cases systematically deprioritized?
Ethical Safeguards
- Are there clear boundaries on what decisions AI may inform?
- Is human oversight documented and enforced?
5. Automated Data Integration & Analytics Tools
Typical Uses
🔄 Merging administrative datasets
📊 Automating indicator tracking
📈 Producing real-time dashboards
⚙️ Building automated data pipelines
Core Evaluation Questions
Data Quality
- How does automation affect completeness & consistency?
- Which assumptions are embedded in data cleaning rules?
Learning & Adaptation
- Do faster insights lead to adaptive management?
- Who has access to dashboards, and who does not?
Sustainability
- What happens when systems break or funding ends?
- Is institutional capacity built alongside automation?
Power & Visibility
- Which indicators become more visible—and which become invisible?
- How does automation influence what gets discussed and acted upon?
Cross-Cutting Questions for All AI Tools
Regardless of tool type, evaluators should consistently ask these fundamental questions that align with OECD-DAC criteria, responsible AI principles, and utilization-focused evaluation approaches.
Whose problem does this tool solve?
Whose values are embedded in system design?
What decisions does the tool influence?
What risks are accepted, and by whom?
How is learning from failure captured?
Why This Mapping Matters for EvalCommunity Users
The UN AI Resource Hub
Gives visibility to what tools exist
This Mapping
Helps determine how to evaluate them
By connecting AI tools to concrete evaluation questions, evaluators can:
Design more credible AI-informed evaluations
Move beyond technical performance metrics
Center ethics, equity, and learning
"AI becomes evaluable, not exceptional."
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