# AI-Generated Analysis Critique Checklist

Use this checklist in Slot 2 when participants review an AI-generated
analysis. The goal is not to catch every coding mistake. The goal is to decide
whether the analysis is trustworthy enough to inform a policy brief.

## 1. Data and indicator fit

- Is the indicator clearly defined?
- Is the denominator appropriate?
- Are the units, year, geography, and population group clear?
- Are missing values, duplicates, and outliers checked before interpretation?
- Does the analysis explain why this indicator was selected?

## 2. Descriptive claims

- Are summary statistics appropriate for the variable type?
- Are means used for skewed data without checking the distribution?
- Are small groups or unstable rates flagged?
- Are visualizations matched to the question?
- Are uncertainty or data quality limitations shown where needed?

## 3. Causal language

- Does the analysis imply causation from correlation?
- Are possible confounders named?
- Are alternative explanations considered?
- Does the conclusion go beyond what the design and data can support?

## 4. Bias and context

- Who may be missing from the data?
- Could reporting quality differ across countries, facilities, or groups?
- Could measurement definitions have changed over time?
- Are equity concerns visible, or hidden by averages?

## 5. Communication and policy use

- Is the main message clear in plain English?
- Are limitations stated without weakening the whole analysis?
- Does the policy recommendation follow from the evidence?
- Is the tone appropriate for decision-makers?
- Would a technical reviewer and a programme colleague both understand the
  message?

## Suggested group task

Ask each group to mark the AI-generated analysis using three labels:

- **Keep:** a claim, chart, or method that is appropriate.
- **Revise:** a useful idea that needs clearer wording, better evidence, or a
  better chart.
- **Remove:** a claim, chart, or method that is unsupported, misleading, or not
  relevant to the policy question.

End with a two-sentence policy brief message:

1. What does the evidence suggest?
2. What should decision-makers do next, and what uncertainty should they keep
   in mind?

