40 Questions Policy Analysts Should Ask Before Trusting AI Summaries
When you send a policy brief or risk assessment to ministers, your professional judgment is what matters, not the AI tool that helped you prepare it. Asking the right questions of your AI outputs protects both your credibility and the quality of political decision-making.
These are suggestions. Use the ones that fit your situation.
1When Claude or ChatGPT summarised those consultation responses, what date was its training data cut off at? Did it see responses filed in the last six months?
2If you asked Perplexity to identify the key disagreements in recent regulatory guidance, can you verify that it found the actual documents or did it reconstruct a plausible-sounding debate?
3Your AI tool flagged three major studies supporting a particular policy position. Can you check whether those studies actually exist or whether the tool generated convincing citations?
4When the AI recommended focusing on stakeholder X over stakeholder Y, what evidence base did it use? Did it have access to internal government correspondence, or only published sources?
5If you fed the AI a government policy paper and it produced a summary, has it missed entire sections because they were in a table or appendix format?
6You asked the AI to compare how three different EU countries implemented a regulation. Is it comparing the official text, or versions the AI saw quoted in secondary sources?
7When Microsoft Copilot pulled in evidence about implementation costs, was it referencing government evaluations or news articles written by people without access to actual programme data?
8The AI suggested that a particular approach has no precedent. Have you checked government archives and previous departmental work that might not be available online?
9If the AI analysed stakeholder positions from a set of documents you uploaded, did you include all relevant stakeholders or only the ones you remembered to search for?
10When the AI produced a timeline of policy changes, does it reflect what actually happened in your department or what was announced and reported in the media?
Questions about what the AI left out
11Your risk assessment now flags three main implementation barriers. What barriers did the AI not mention because they are political rather than logistical?
12The AI summary of stakeholder opinion treats the responses as data points. Which organisations will oppose this policy based on funding relationships or ideological positions rather than technical arguments?
13When you asked the AI to identify the strongest objection to a proposed regulation, did it find the real objection that powerful actors are making privately, or the objection that appears most in published statements?
14The AI recommended a policy approach as lower-cost than the alternative. Does this account for the cost of change management and staff retraining in your own organisation?
15Your brief now says there is consensus on a particular point. Have you checked whether silence from certain stakeholders means agreement or simply that they have not yet mobilised?
16The AI identified best practice from another department. What was tried in your own department before that was abandoned for reasons that might not be documented?
17When the AI listed options for managing this issue, did it miss the option that is politically impossible even though it would be technically effective?
18The risk analysis now ranks certain outcomes as low-probability. Have you checked whether those outcomes simply have not happened in the available data because no one has tried this approach before?
19Your brief presents a regulatory obligation as straightforward to implement. What assumptions about staff capacity and capability is the AI making?
20The AI summary treats the evidence for a policy approach as settled. Which experienced civil servants in your own organisation have seen similar policies attempted and can tell you what went wrong?
Questions about how the AI reasoned
21When ChatGPT recommended this approach, did you ask it to explain which studies it weighted most heavily and why? Does that weighting match your department's actual priorities?
22The AI produced a cost-benefit analysis that favours option A. Have you tested whether flipping the assumptions (different cost estimates, different timescales) would change the recommendation?
23Your risk assessment lists implementation difficulty as a major concern. Is the AI assessing difficulty objectively or reproducing the difficulty that other departments reported when they faced similar mandates?
24When the AI compared this policy to similar policies elsewhere, what made it choose those comparisons? Would a different set of comparisons support a different conclusion?
25The AI suggested that a particular stakeholder concern is unlikely to materialise. Is that based on evidence or on the frequency with which that concern appears in the documents you fed it?
26Your brief now concludes that option B is more feasible than option A. Can you separate what the AI found in the sources from what it inferred?
27When Perplexity synthesised different expert positions, did it weight them equally or did it weight them by how often they appeared in the sources it could access?
28The AI identified a particular risk as critical. Is this because the risk is genuinely large or because it is the type of risk that gets discussed in policy papers and academic journals?
29Your summary now says that most respondents to the consultation supported this approach. Did the AI count responses equally or weight them by the size of the organisation making them?
30When the AI drew a conclusion about what will work, was it reasoning from evidence about what has worked before or was it generating a plausible-sounding answer?
Questions about what happens next with your judgment
31If ministers act on this AI-informed brief and it goes wrong, can you trace the failure back to the AI's reasoning or will it appear to be a failure of your departmental judgment?
32Are you using this AI summary because it is the best way to understand the issue or because it is faster than reading the source documents yourself?
33Which parts of this brief would you defend in front of Parliament if you had to explain the reasoning in detail?
34If a select committee asked you to produce the evidence base for this policy recommendation, which parts of that evidence base came from the AI tool rather than from your own research?
35Have you shared this AI-generated analysis with colleagues who have direct experience in this policy area and asked them whether the framing is missing something important?
36When you hand this brief to your director, are you confident enough in the analysis to defend it if the assumptions turn out to be wrong?
37If this policy is implemented and creates unexpected resistance from staff or the public, will you be able to identify whether the resistance was unpredictable or whether the AI simply did not flag it?
38Have you identified which parts of this analysis depend on the AI's reasoning and which parts depend on your own expertise and institutional knowledge?
39If you were to brief on this policy without using the AI summary, what would you say differently?
40Is there someone in your organisation who should see the raw source documents before this brief goes forward, even though the AI has already summarised them?
How to use these questions
When an AI tool produces a policy summary that feels complete, that is often a sign that it has smoothed over disagreement that should inform political judgment. Check the source documents for conflict.
If multiple policy analysts are using the same AI tool with similar prompts, your department is producing homogenised advice with a single point of failure. Assign one person to challenge the consensus.
Risk assessments from AI tools optimise for measurable risks. The risks that matter most in policy implementation are often irreducibly political. Talk to staff who have managed change before.
When experienced civil servants leave and are replaced by prompt engineers, institutional memory walks out the door. Before you delegate analysis to AI, record what you are delegating and why only the AI can do it.
An AI citation that looks authoritative is often a hallucination. Verify quotes, dates, and source attribution before you send anything to ministers. One false citation in a policy brief damages your credibility permanently.