Considerations and Practical Applications for Using Artificial Intelligence (AI) in Evaluations

The CGIAR 2030 Research and Innovation Strategy commits organizational change with seven ways of working, including “Making the digital revolution central to our way of working”. In that context, Artificial Intelligence (AI), introduces both opportunities and risks to evaluation practice. Guided by the CGIAR-wide Evaluation Framework,integrating AI tools requires a governance approach to balance innovation with ethical responsibility, ensuring transparency, fairness, accountability, and inclusivity. This Technical Note encourages and guides CGIAR evaluators to ethically explore, negotiate, and experiment with AI tools:

Ethical AI governance should be embedded in the entire evaluation lifecycle. During the design phase, evaluators should define AI tools to use, why they are selected, and assess risks. In data collection, AI tools should be used in ways that uphold data privacy and protection standards and avoid reinforcing harmful stereotypes or excluding groups. During the analysis phase, the role of AI in supporting interpretation should be documented, with an acknowledgment of limitations or biases. In dissemination, documentation, and reporting, AI’s contribution, limitations, and human validation should be disclosed.  By rapid adaptation of content across formats, languages, and complexity levels, AI opens possibilities for broader, more inclusive communication of findings.   Finally, the follow-up phases should include a reflection on the ethical implications observed and how these lessons can improve future evaluations.

By embedding methodological flexibility into the evaluation processes, AI adoption would contribute to integrity, equity, and learning in an era of rapid technological advancement. This Technical Note is a conversation starter—as a “Beta” version, it will evolve based on responsible real-world experimentation and continuous reflection. Evaluators are encouraged to be responsive to stakeholder input throughout the evaluation processes, to ensure relevance, accuracy, and inclusivity.