What does "AI must belong to everyone" mean for evaluators?

AI governance debates often happen far from the realities of development work, humanitarian settings, and evaluation practice. For EvalCommunity users, "AI for everyone" means transparent algorithms in targeting, scoring, and risk models. It requires explainable AI that evaluators can audit, question, and validate. It demands shared evidence, not proprietary black boxes driving public policy. Replacing hype and fear with shared evidence is, at its core, an evaluation mandate. M&E professionals are uniquely positioned to test AI claims, assess unintended impacts, and separate real value from inflated promises.

• ▹ Transparent algorithms enable evaluator scrutiny.
• ▹ Explainable AI supports audit and validation.
• ▹ Shared evidence prevents black-box policymaking.
• ▹ AI without accountability is not innovation—it is risk.

Why is accessibility critical for evaluation capacity?

Without deliberate investment, many countries—and many evaluation teams—will simply be logged out of the AI age. This has direct consequences for national evaluation systems, local research institutions, small NGOs and consulting teams, and independent evaluators in the Global South. A Global AI Fund, as proposed by the UN, would matter enormously for training evaluators in AI literacy, providing access to affordable computing power, building local data infrastructure, and creating inclusive AI ecosystems beyond elite institutions. For EvalCommunity, this reinforces a core principle: capacity building is governance.

• ▹ National evaluation systems require AI literacy investment.
• ▹ Local institutions need affordable computing access.
• ▹ Global South evaluators must not be excluded.
• ▹ AI access must not become donor-driven asymmetry.

How can AI benefit everyone in development contexts?

Done right, AI can support faster evidence synthesis, better forecasting for food security and climate risk, improved targeting of social programs, real-time learning in adaptive management, and stronger monitoring of SDG progress. But done wrong, it can encode bias into beneficiary selection, marginalize communities already under-represented in data, and justify harmful decisions with "algorithmic authority." For evaluators, this means AI systems themselves must become objects of evaluation: Who benefits? Who is excluded? What trade-offs are hidden? What assumptions are embedded?

• ▹ AI accelerates evidence synthesis and forecasting.
• ▹ Bias in beneficiary selection must be monitored.
• ▹ Algorithmic authority requires independent scrutiny.
• ▹ AI should accelerate SDGs, not redefine success.

What sustainability and ethics considerations matter for M&E?

AI's energy and water demands are growing fast. Data centres, supply chains, and compute-heavy models have real environmental costs. From an evaluation perspective, environmental externalities must be measured, climate impacts must be attributed, and costs must not be shifted to vulnerable communities. Likewise, labor impacts matter: AI should augment human expertise, not erase livelihoods. Evaluators must examine displacement risks, and workforce transition must be part of program logic. If AI "efficiency" comes at the expense of dignity, it fails the development test.

• ▹ Measure AI's environmental externalities.
• ▹ Attribute climate impacts to AI systems.
• ▹ Examine labor displacement and workforce transition.
• ▹ Efficiency must not come at dignity's expense.

How does safety apply to AI in evaluation contexts?

Unregulated AI systems are already being tested in education, welfare, migration, and policing—often with minimal oversight. The UN's message is clear: no child should be a test subject for unregulated AI. For EvalCommunity users, safety means strong ethical review mechanisms, clear accountability lines, human oversight at every decision point, and safeguards against manipulation, surveillance, and abuse. Evaluation frameworks must evolve to assess algorithmic harm, not just program outputs. This requires new competencies and protocols within M&E practice.

• ▹ Ethical review mechanisms must assess AI applications.
• ▹ Human oversight required at all decision points.
• ▹ Safeguards against manipulation and abuse.
• ▹ Evaluate algorithmic harm, not just outputs.

What does this vision mean for EvalCommunity's role?

EvalCommunity sits at a critical intersection of evidence, accountability, learning, and governance. AI governance cannot succeed without evaluators who understand AI systems, standards tailored to development contexts, ethical frameworks grounded in real-world practice, and independent scrutiny beyond tech vendors. The future of AI will not be decided by code alone. It will be decided by who measures, questions, and governs its impact. For the evaluation and international development community, the task ahead is clear: treat AI as a public good, embed ethics into evidence systems, and ensure technology serves people—not the other way around.

• ▹ Evaluators must understand AI systems deeply.
• ▹ Standards must fit development contexts.
• ▹ Independent scrutiny beyond vendors is essential.
• ▹ Governance requires evaluation-literate practitioners.

Frequently asked questions about Guterres' AI vision and evaluation

Authoritative resources and further reading