Climate decision-makers increasingly encounter AI in analysis, communication, and software interfaces. Useful literacy does not mean learning every technical detail. It means recognising what the system is doing, what evidence supports it, and where responsibility for the decision remains.
Distinguish the types of task
An AI system might classify an image, estimate a physical variable, retrieve a document, or generate an explanation. These tasks have different failure modes and evaluation needs. Ask the provider to identify the task clearly. A fluent explanation does not demonstrate the accuracy of a physical estimate, and a good classifier is not automatically a dependable adviser.
Ask about evaluation and boundaries
Useful questions include: what data was used, how was the system tested, what baseline was compared, and which conditions were not covered? Ask what happens when the system lacks evidence. Be cautious about a single accuracy percentage that is not linked to a defined task, dataset, and consequence of error.
Keep responsibility explicit
Decide which outputs require review, who has the relevant expertise, and how disagreement is recorded. Make it possible to inspect sources and correct errors. Review the workflow after it has been used, not only during procurement. Effective AI literacy is a continuing practice of asking clear questions and learning from the answers.
Three things to take away.
- Identify whether the system predicts, classifies, retrieves, or generates.
- Ask for task-specific evaluation and known limitations.
- Keep review authority and responsibility visible.
Further reading
Explore the underlying topics through these reference sources.