AI can help governments process more public input by transcribing, translating, grouping and summarizing contributions. But those tasks do not establish whose views are represented, which concerns deserve weight or how citizens’ input should affect a decision. Used responsibly, AI is an administrative aid: officials and participants must be able to check its interpretations, and governments must explain what happens to the input.
What AI can do with public comments
Consultations can produce more responses than staff can read closely within the time available. AI tools can help organize that material: converting speech to text, translating contributions, grouping related comments, identifying recurring topics and outliers, and drafting summaries. Some tools also support online deliberation or help participants understand other contributions. The OECD describes these uses across participation design, accessibility, implementation, interpretation and communication in its 2025 review of 50 AI use cases across 22 member and partner countries.
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These outputs are interpretations, not a count of how many people hold a view or a ruling on how important it is. A cluster can make a recurring theme easier to find, but the model’s categories and summary can also obscure disagreement, unusual arguments or the context behind a comment. Sentiment analysis, in particular, should not be treated as a measure of public support. The OECD describes AI as a support tool, “not as a replacement for skilled practitioners and participants,” in its discussion of AI and citizen participation in policymaking.
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What government pilots show—and what they do not
Examples show that AI can reduce the effort of handling contributions, but they differ in task, maturity and evidence. Pilot speed figures are specific to the reported work, not general benchmarks.
#1 Best Overall
| Example | What it does | Evidence and status |
|---|---|---|
| UK Department for Transport Consultation Analysis Tool | Generates themes and maps consultation responses for analysis. | The 2025 evaluation compared generated analysis with human-analysed benchmarks and examined a live pilot in which reviewers could amend the tool’s initial analysis. It also considered whether accuracy varied across proxies for protected characteristics. The published evaluation summary does not provide all numeric accuracy results. |
| PlanAI local-planning pilot | Groups comments by policy area and produces summaries by chapter, policy or site. Respondents can amend an “instant” summary before submitting. | The UK government reported that an initial pilot summarized three planning consultations in 16 minutes, compared with 18.5 hours of manual work. Its February 2026 update said five more local planning authorities had begun testing the tool, with officers reviewing summaries. The programme also reported £45,000 in additional funding to stress-test it with those authorities. These are pilot-specific results and plans, not evidence of typical performance across consultations. |
| OECD-supported prototypes | Prototype approaches to AI-enabled citizen participation. | Examples in the OECD’s account include work in the Netherlands, Portugal and Spain that was still being tested and refined before deployment. Their status should not be confused with established, widely deployed services; see the OECD discussion of fit-for-purpose tools. |
The examples demonstrate possible uses and evaluation approaches, not that AI has made consultation representative, removed bias or increased policy influence across governments. Comparable evidence across jurisdictions—including language performance, which participants are reached and whose contributions affect outcomes—remains limited.
Start with the decision, not the tool
AI cannot make a consultation meaningful if officials have already decided the issue or cannot change course. The OECD’s 2022 Guidelines for Citizen Participation Processes say participation should be organized only where there is room for meaningful citizen input in decision-making. The guidelines set out 10 steps and eight participation methods, including planning for outcomes, identifying participant groups and choosing accessible ways to take part.
Before selecting an AI system, officials should make clear what people can influence, which groups need to be involved, what decisions remain open and what result the process is meant to achieve. A high response count does not by itself show that participants reflect the affected public—or that their input had influence.
Keep people able to inspect and challenge AI’s interpretation
Participants should be told what the system will do with their input: for example, transcribe it, translate it, assign it to a topic or summarize it. Where possible, give contributors a way to correct a transcript or summary, and tell them how to contest a classification. PlanAI’s option to let a respondent amend an AI-generated summary before submission is one example of such a correction route. In the Department for Transport’s tool evaluation, human reviewers could amend the initial analysis in the live pilot; the evaluation page also describes comparison with human benchmarks.
Rank #3
Human review is not just a final proofread. Reviewers need access to the original contributions and enough time and context to spot when a tidy summary has flattened an important distinction. Officials should be able to explain how categories were chosen, how unusual or conflicting comments were handled, and where a summary is uncertain. A polished output is not proof that the input was faithfully represented.
Check where voices can disappear
Governments can use the following questions to assess a consultation’s whole participation and analysis process. They are practical checks, not a universal audit standard or claims that existing pilots implement every item.
- Who could participate? Were recruitment and outreach sufficient to reach people beyond those who routinely respond online?
- What could people submit? Which languages, accessible formats and contribution methods were supported, and what did the tool fail to process?
- How were submissions treated? How were duplicates, coordinated campaigns, long responses and unusual formats handled?
- How did analysis shape the result? Who chose the categories? Were minority positions and outliers preserved, or absorbed into broader themes?
- Could participants correct errors? Was there a clear route to challenge mistranscription, mistranslation, categorization or a misleading summary?
- Was performance checked across groups and inputs? What human benchmark was used, what did it cover, and where was accuracy or coverage weaker?
- Can the public see what followed? Does the report show how input was considered and what decisions remain for officials?
These questions reflect OECD guidance on inclusion and accessibility, alongside the evaluation and transparency practices described in the government examples. They help distinguish a system that processes volume from a process that lets people participate meaningfully.
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Tell participants how their input was received, share findings at a level suited to the process, and explain what happens next. The UK Government Communications toolkit advises: “Consider using AI to collate and summarise large volumes of feedback but always check any outputs for accuracy.” Its guidance also emphasizes communicating how input was received and what will follow; see the 2026 Communications and citizen participation toolkit.
Reporting back is part of the participation process, not a courtesy after the analysis is done. If a government cannot show how themes informed a decision—or why a proposal was not changed—citizens cannot tell whether the consultation mattered.
Public confidence depends on the use case and the process
People may react differently to AI analyzing consultation responses than to AI drafting correspondence, so governments should explain the specific task rather than make a vague claim that they are “using AI.” The Department for Transport’s 2023 deliberative research examined both use cases through three public workshops attended by 71 people in England, Scotland and Wales, 10 interviews with people who had recently engaged with UK consultations or correspondence, and nine stakeholder interviews. These findings can inform how authorities communicate about a use case, but this was deliberative research—not a representative national poll. Details are on the DfT public-attitudes research page.
That evidence also points to a broader limit: people need to understand what the system does and how a human remains accountable for the decision. Trust is not secured by saying that a tool is efficient or supervised; participants need a way to understand and question how their own contributions were handled.
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