Ethical Usage of AI for Efficient Evaluation

Artificial intelligence is no longer an external tool in monitoring and evaluation; rather it is becoming the backbone through which programme data is collected, analysed, and acted upon. However, the pace of adoption has surpassed the development of the ethical frameworks required to ensure that AI benefits the intended populations. The integration of AI in evaluation is not a constraint on efficiency but it has become the foundational requirement for producing findings that are more credible, reliable, equitable, and actionable.
The ethical usage of AI in evaluation must be human-centred. Evaluation is not just about measuring outputs; rather it is about understanding its impacts on people, particularly vulnerable sections of the society. If AI systems are used carelessly, they risk reinforcing existing inequalities hidden within data which is feeded to the system based on the existing literature and datasets. Biased datasets or poorly designed algorithms can lead to skewed findings, which in turn may shape policies that unintentionally exclude or disadvantage certain groups. Therefore, AI must enhance representation rather than diminish it, ensuring that true efficiency does not come at the cost of fairness.
A key ethical principle of AI is augmented intelligence, not automated judgment. The role of the AI should be to assist evaluators, and not substitute them. AI can identify patterns across large datasets but it lacks contextual understanding such as social, cultural, and political nuances that are central to scheme evaluation. Therefore, maintaining a human-in-the-loop ensures that insights are interpreted responsibly and aligned with ground realities. This hybrid approach improves both efficiency and validity of an evaluation.
Transparency and explainability are equally critical. If AI models run as a black box, stakeholders cannot trust or validate conclusions. Ethical AI demands that methodologies, data sources, and assumptions are clearly documented and communicated in accessible terms. Another dimension is data ethics and privacy. M&E systems increasingly rely on granular, often sensitive data-household surveys, geospatial data, or administrative records. Ethical AI requires a shift from data maximisation to data responsibility by collecting only what is necessary, anonymising it, and ensuring secure handling. This is especially important when dealing with vulnerable communities, where misuse of data can lead to harm beyond the evaluation itself.
Importantly, ethics should be embedded across the entire evaluation lifecycle, not treated as a compliance checklist. From designing evaluation matrices to interpreting results, each stage must consider potential limitations, harms, trade-offs, and unintended consequences and explicitly explain the same in the evaluation reports. The UNESCO Ethical AI framework emphasises that ethical reflection must accompany every stage of AI deployment.
The consideration of the ethical aspect of AI integration in the evaluation is an important aspect and a standard framework on the ethical usage of AI should be in a place to drive the evaluation studies efficiently. This would allow the evaluations powered by AI to remain transparent, inclusive, accountable, and human-centred. Only then can AI truly strengthen evidence-based policymaking while upholding the core values of evaluation practice.