Can AI Understand a Community?

Artificial intelligence is quickly becoming part of the toolkit for community engagement. In the past year alone, I have seen local governments, planning agencies, consultants, and nonprofit organizations begin experimenting with AI to summarize public comments, identify themes across surveys, draft outreach materials, translate content, and support public participation efforts.

The enthusiasm is understandable. Anyone who has managed a community engagement process knows the challenge. By the end of a project, you may have hundreds of survey responses, dozens of stakeholder interviews, pages of meeting notes, and comments collected through public events, workshops, and online platforms. The information is valuable, but making sense of it all requires time that many organizations simply do not have. AI offers a way to accelerate that process and uncover patterns that might otherwise be overlooked.

At the same time, community engagement has never been solely about information collection or extraction. It is about understanding people. As AI becomes more integrated into engagement processes, practitioners will need to understand both where it adds value and where its limitations become apparent.

How Organizations Are Beginning to Use AI

Some of the earliest examples are emerging from governments and civic organizations seeking to manage large volumes of public input. 

The City of Helsinki has experimented with AI-supported tools to help review citizen feedback and improve government responsiveness. 

Taiwan has become known for its use of digital democracy platforms that combine technology and human facilitation to identify areas of consensus among residents. 

The conversation is evolving quickly, and earlier this month in Planning magazine, journalist Patrick Sisson explored how AI is beginning to influence not only the analysis of public input, but the input itself. 

The City of Helsinki is exploring how AI can support and improve public services and decision-making.

The article highlights concerns that AI-generated submissions may have contributed to more than 20,000 public comments during a regulatory debate in Southern California, raising questions about authenticity, representation, and influence. 

As Senator Christopher Cabaldon asked in the piece, "As a mayor or a senator, how do I know what really matters to the public anymore?" For a profession built on public participation, that may become one of the defining engagement questions of the next decade.

What once required weeks of manual review can now happen in hours. For organizations facing limited budgets and growing expectations for public participation, efficiency creates opportunities. More time can be spent engaging residents, and less time can be spent sorting spreadsheets or coding transcripts. The question is not whether AI will become part of engagement work. It already has. The more important question is how it should be used.

Where AI Adds Value

AI may be most useful in the parts of engagement work that require significant time but do not always require original human judgment. It can help organize large volumes of comments, identify recurring themes, draft plain-language materials, and create first-pass summaries that allow practitioners to spend more time interpreting what they are hearing.

For small communities with limited staff, these applications may be particularly valuable. A rural municipality with one planner and a part-time communications staff member can suddenly access analytical capacity that previously existed only within larger organizations. Through my work with the Crawford County, Pennsylvania, Planning Commission, I have seen how government-approved AI tools can help stretch limited staff capacity while remaining consistent with local policies governing responsible AI use.

AI may also help reduce barriers to participation. Communities are increasingly multilingual, yet engagement materials are often produced in only one language because translation costs remain prohibitive. AI tools are making it easier to create materials in multiple languages, draft simpler explanations of technical issues, and communicate information in formats that are more accessible to different audiences.

I learned this firsthand while recovering from two carpal tunnel surgeries this year. There were days when typing was painful, slow, or simply not possible, and AI-powered transcription and voice tools allowed me to continue working in ways I otherwise could not have. It was a reminder that although AI is often discussed in terms of efficiency, some of its most meaningful applications relate to accessibility and expanding who can participate and contribute.

The Risk of Mistaking Patterns for Understanding

Anyone who has spent enough time facilitating public meetings knows that people rarely fit neatly into categories. Residents talk about housing while expressing concerns about belonging. They talk about traffic, yet the deeper concern is often the pace and impact of growth.

They talk about workforce shortages while describing broader concerns about quality of life, and the words people use are often only part of the story.

AI can identify that housing was mentioned 500 times. It cannot fully understand why housing has become such an emotional issue in one community and not another. It cannot understand decades of local history, community relationships, or the trust that has been built between institutions and residents.

Community engagement requires making sense of what people are saying, what they are not saying, and how different perspectives connect. Focusing on only one source of information creates an incomplete picture.

AI can surface patterns in data, but understanding what they mean still depends on human interpretation and context.

The Illusion of Objectivity

Much of the public discussion around AI focuses on bias, and for good reason. AI systems learn from existing information. If that information contains historical biases, underrepresentation, or skewed perspectives, those patterns can appear in the outputs. Communities that have historically struggled to have their voices heard may face similar challenges within AI-supported processes if practitioners are not thoughtful about how these tools are applied.

And bias is not unique to AI–every engagement process involves choices. We decide where meetings occur, what questions are asked, who receives invitations, and how findings are summarized. Human judgment shapes every stage of engagement.

The difference is that AI can create the appearance of objectivity but not actually have it. A computer-generated summary often feels more authoritative than a facilitator's notes, even when both contain assumptions and limitations. That perceived neutrality may be one of the greatest risks associated with AI in community engagement.

Finding the Right Balance

AI can support engagement practitioners by helping them process larger volumes of information more efficiently. There is no doubt that it can do this faster than humans. 

However, humans bring something different. They understand local context. They recognize contradictions. They build trust. They ask follow-up questions when something feels incomplete. They notice what was not said.

Community engagement has always been about the relationships we create. That remains true regardless of how sophisticated technology becomes.

At Fourth Economy, we're continuing to explore where AI can strengthen planning, engagement, and strategy work while keeping people at the center of the process. We'd welcome the opportunity to learn from others navigating the same questions.

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