Introduction to AI Governance Toolkit
AI Governance Toolkit for M&E: risk, fairness & trust in practice
Why AI governance matters in development programmes: Unchecked AI systems can reproduce discrimination, violate privacy, and produce unreliable outputs. Structured governance helps organisations identify and mitigate these risks before deployment. This toolkit translates high‑level frameworks (NIST AI RMF, OECD AI Principles) into daily M&E workflows.
Eight integrated tools, one workspace
Risk Register
Document & score AI risks
RACI Matrix
Map governance roles
Red Teaming Planner
Stress‑test models
Fairness Evaluator
Bias & equity checks
Metrics Explorer
126‑metric database
Ethics Assessment
Values‑alignment
Governance Framework
Policy builder
Monitoring Dashboard
Real‑time oversight
← all tools work offline, data stays in your browser. Open toolkit.
What is included in the AI governance toolkit?
The toolkit contains eight interconnected modules that cover the full AI governance lifecycle: identification (risk register), planning (RACI, red teaming), measurement (fairness, metrics), and continuous monitoring. Each tool is pre‑filled with examples from development sectors (health, education, social protection) and aligns with NIST AI RMF functions: Govern, Map, Measure, Manage.
How does the AI risk register builder work?
Users can document AI‑related risks (e.g., bias, privacy, transparency) and score them by impact × likelihood to obtain a risk priority number. The register follows the structure recommended by the NIST AI RMF and allows filtering by category, status, or owner. Entries can be exported to CSV or Excel for reporting.
- Risk scoring: 4×4 matrix (impact 1–4, likelihood 1–4).
- Pre‑loaded examples: beneficiary targeting bias, health data privacy, model drift.
- Export: one‑click CSV/Excel for donor reports.
- Alignment: maps directly to NIST MAP and GOVERN functions.
Why is trustworthy AI evaluation important for M&E?
Trustworthy AI metrics provide quantitative evidence that systems behave safely, fairly, and transparently. For M&E professionals, this means being able to verify that an AI‑assisted evaluation does not produce skewed findings. The toolkit’s Metrics Explorer contains 126 operationalised metrics (demographic parity, SHAP explainability, calibration, etc.) drawn from OECD and academic sources.
How can I use the fairness evaluator in development programs?
The fairness evaluator guides users through a step‑by‑step assessment: select protected attributes (gender, region, ethnicity), choose fairness metrics (equal opportunity, predictive parity), and interpret results. It references the UNESCO Recommendation on AI Ethics and includes remediation suggestions (re‑weighting, threshold adjustments).
Frequently asked questions – AI Governance Toolkit
| Do I need an account or installation? | No. Everything runs in the browser; no data leaves your device. You can start immediately. |
| Which frameworks are aligned with the toolkit? | NIST AI RMF, OECD AI Principles, EU Ethics Guidelines for Trustworthy AI, and UNESCO AI Ethics. |
| Can I export the risk register for donor reporting? | Yes, the risk register exports to CSV or Excel, ready to be attached to reports or shared with partners. |
| Is the toolkit suitable for non‑technical M&E officers? | Absolutely. The tools use plain language, examples, and guided workflows — no coding required. |
| How are the 126 metrics sourced? | From academic literature, NIST, OECD, and leading AI fairness libraries (AIF360, SHAP). |
Authoritative resources & standards
- NIST AI RMF 1.0
- OECD AI Principles
- UNESCO AI Ethics
- World Bank AI & development
- EvalCommunity home
- AI in M&E course (academy)
Start using the AI governance toolkit today
From risk registers to fairness metrics, the EvalCommunity AI Governance Toolkit equips M&E and development professionals with practical, framework‑aligned instruments. No sign‑up, no cost — just open‑source style utilities for responsible AI in international development.
