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AI can help public agencies transcribe, translate, group and summarize large volumes of citizen input. But processing more comments is not the same as representing people fairly: officials still have to decide what the public can influence, check what the system produces and explain how contributions affected the decision.
What can AI do with public consultation responses?
AI can support several stages of a participation process: making input easier to provide, helping people understand other views, and assisting officials with analysis and communication. For consultation responses, its most direct uses are transcription, translation, topic grouping and draft summaries. These can help staff identify recurring issues across a large set of submissions, while keeping individual contributions available for review.
The OECD’s 2025 report surveys 50 AI use cases across 22 member and partner countries. It describes applications in participation design, accessibility, implementation, interpretation and communication—not just automated analysis. That is a landscape review of varied cases, not evidence that every tool works equally well or that any one approach makes a process representative. OECD, Artificial Intelligence and the Future of Citizen Participation (2025)
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Why does processing more input not guarantee fair representation?
A system can process a high volume of comments and still leave important voices out. Some people may not know about a consultation, be able to access its format, or have the time and language support needed to take part. Others may contribute in ways the tool handles poorly. Even when the analysis is accurate, a summary can obscure disagreement, unusual arguments or the context behind a position.
Participation also needs a real connection to a decision. The OECD’s 2022 guidelines state: “Citizen participation processes should be organised only when there is room for meaningful citizen participation in the decision-making process.” They set out 10 steps and eight participation methods to help public bodies plan for clear outcomes, appropriate participant groups, inclusion and accessibility. The principle is practical: define what citizens can influence before selecting a tool to process their input. OECD Guidelines for Citizen Participation Processes (2022)
A large response count by itself does not show that participants reflect the affected public, that less common views remain visible, or that officials considered the input. Those are questions about the design and conduct of the process, not problems a text-analysis model can solve on its own.
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What do government pilots show so far?
Published UK examples show ways AI might assist consultation work, alongside the need to treat results as bounded evidence. They differ in task, stage and maturity; none establishes that AI consultation analysis is universally accurate or representative.
| Example | What the tool or study did | Evidence and scope |
|---|---|---|
| UK Department for Transport Consultation Analysis Tool | Generated themes and mapped consultation responses for human review. | The 2025 evaluation compared the tool’s analysis with human-analysed benchmarks, examined a live pilot in which reviewers could amend initial analysis, and considered whether accuracy varied across protected-characteristic proxies. The evaluation page summary does not provide all numeric accuracy results. DfT, AI Consultation Analysis Tool evaluation (2025) |
| UK local planning PlanAI pilot | Grouped comments by policy area and created summaries by chapter, policy or site; an instant-summary option let respondents amend a summary before submission. | In an initial pilot result reported in February 2026, three planning consultations were summarized in 16 minutes, compared with 18.5 hours of manual work. This is a reported pilot comparison, not a general benchmark. Five additional local planning authorities had begun testing the tool, with officers reviewing summaries; the programme reported £45,000 in additional funding for that stress test. UK MHCLG Digital Planning (17 February 2026) |
| OECD-supported prototypes in the Netherlands, Portugal and Spain | Explored AI-enabled approaches to citizen participation. | The OECD’s 2025 account describes the prototypes as still being tested and refined before deployment; it does not establish results from broad operational use. OECD, What can governments do? (2025) |
These cases illustrate different forms of human involvement: reviewers can check generated analysis, and respondents may be able to edit a summary of their own contribution. They do not establish one standard accuracy level across tools, languages or consultation types.
How should officials protect minority and outlier views?
Fairness depends on the whole participation pipeline, from recruitment to the final account of what decision-makers heard. The following checks are practical questions for a public body to answer when planning and evaluating a process; they are not claims that every existing system already performs them.
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- Who can take part? Consider who is affected by the decision, who is likely to hear about the process, and whether people can participate through accessible routes beyond an online form.
- What can the system process? Make clear which languages, formats and contribution lengths are supported. Look for ways a translation, transcription or classification error could change meaning.
- How are contributions treated? Examine how duplicates, organized campaigns, unusually long submissions and comments outside the chosen categories are handled. Multiple similar submissions may matter, but counting them is not the same as measuring the full range or strength of opinion.
- Can people challenge an output? Tell contributors whether AI will transcribe, translate, classify or summarize their input, and offer a way to correct a transcription or contest a misleading summary or category.
- Do reports preserve disagreement? Check whether minority positions and outliers remain visible in the analysis rather than disappearing into a dominant theme. Compare summaries with source material and include human review.
- Can the process be evaluated? Set out what will be checked, against what evidence and for which participant groups, languages and formats. The DfT evaluation’s use of human benchmarks and its examination of protected-characteristic proxies show examples of questions an evaluation can investigate; they do not provide a universal audit protocol.
OECD guidance on participation design and its discussion of fit-for-purpose tools emphasize inclusion, accessibility and accountable use. The evidence does not establish a single audit method that will work for every government or consultation. OECD, What can governments do? (2025)
How can governments build trust and close the loop?
Participants should be told what AI is doing with their input, what people will check and what happens after the consultation closes. A summary should not be presented as a complete record if it compresses or categorizes contributions; where possible, contributors should be able to correct errors in how their own input was captured or represented.
The UK Government Communications toolkit advises: “Consider using AI to collate and summarise large volumes of feedback but always check any outputs for accuracy.” It also recommends informing participants how their input was received, sharing findings at an appropriate level and explaining what happens next. A public body can listen at scale yet still fail to make participation meaningful if it never shows how contributions were considered. UK Government Communications, Communications and citizen participation toolkit (2026)
What do public attitudes and current evidence tell us?
The Department for Transport’s 2023 research examined public attitudes to AI for consultation analysis and for helping draft responses to external correspondence. It included three workshops attended by 71 members of the public in England, Scotland and Wales, 10 interviews with people who had recently engaged with UK consultations or correspondence, and nine stakeholder interviews. These deliberative discussions help show why explanation and the specific use case matter; the sample was not a representative national poll. DfT, Public attitudes to the use of AI in DfT consultations and correspondence (2023)
The broader evidence is still developing. OECD’s case survey spans many initiatives, while the UK examples include pilots, evaluations and further testing. Comparable measurements across governments of accuracy, language performance, participant coverage and the influence of AI-processed input on final decisions are not established in these sources. It is therefore not supported to claim that AI has already made consultation broadly representative, eliminated bias or increased policy influence everywhere. OECD, AI in civic participation and open government (2025)
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