When it comes to using generative AI in local government—technology that can draft text, summarize documents, or answer questions on demand from simple prompts—bias, privacy issues, and a lack of transparency aren’t just theoretical risks. Without clear rules for selection and usage, these risks are inevitable.

That’s why local governments need an AI governance framework that spells out what’s ok, what’s not, and how decisions should get made when it comes to using AI.

Building an Effective AI Governance Framework

No framework is possible without clarity.

What problems are we trying to solve by using the technology?

What are the benefits we’re trying to bring to the users of our technology?

How will constituents benefit?

What do we value in terms of ethics and morals?

To govern how you will use AI, your organization needs to answer these questions. And then experts can begin the task of building the framework.

The AI governance committee, a group of people from a variety of disciplines and with diverse expertise, will attack the use of AI from a variety of angles. Through debate, discussion, and the use of pre-existing knowledge, the group builds the guidelines. They will create a framework that reflects the organization’s values and that solves the stated problems, while delivering the identified benefits to both the internal users and to the constituents who will receive the output.

Of course, framework development is one part. Execution is the other half of the equation - the framework needs to translate to action.  Usually a coordinator who can guide the process across departments, usually someone from IT, pushes the technology and the policy governing its use across departments. The coordinator and their team help users understand how to use the AI and confirm that everyone’s working from the same playbook.

AI Governance in Action

Once AI governance framework is set, its usefulness is immediately apparent, especially across the dimensions of vendor selection and procurement, data governance, transparency, and bias and equity.

Vendor selection and procurement

Not every AI vendor meets the standards your organization cares about. A framework lets you sort through the technologies you’re considering and evaluate them to ensure that they conform with your framework and policy. Speak directly with  each vendor. Ask how their systems support your internal policies, how they handle data privacy, and what the short- and long-term costs look like. Ensure that the tools you choose are both technically sound and aligned with your values from the start.

Data governance

AI relies on data—but not all data is fit for use with AI. Your AI governance framework should define which types of data can be used, how that data should be structured, and what rules must guide the data’s use.

When you know which data can be fed into AI, how to safeguard it, and how to align its use with your community values, you're no longer just managing data. You're setting the foundation for responsible AI.

Transparency

Everyone should understand how the organization uses AI, and what decisions, if any, AI is involved in. That’s why sharing the AI governance framework publicly is essential. All stakeholders need to understand how the technology is being used so that the information produced by the AI is trustworthy and understood.

Bias and Equity

AI systems are trained. A good AI framework ensures that during the implementation and training of the systems, your organization avoids using flawed data and introducing bias. The system works fairly from the start and continues to work fairly.

Regular reviews will allow you to identify unintended outcomes early. That ongoing monitoring is exactly what the NIST AI Risk Management Framework recommends to help organizations “identify, manage, and reduce” bias.

From Planning to Practice: King County’s Approach

King County, Washington offers a clear example of a comprehensive AI framework that continues to evolve. Instead of treating governance as a one-time task, King County, Washington, built a living system—one that guides real decisions and adapts as technology evolves.

In 2024, King County released official guidance for using generative AI responsibly. The Department of Technology and the Office of Equity and Social Justice led the effort, bringing together staff from across departments to define key priorities—protecting confidential data, minimizing bias, and aligning with countywide equity goals.

Based on those priorities, they created clear rules: only approved tools that meet internal standards can be used, and confidential information must never be entered into generative AI systems. Then they put the policy to work. They used it to evaluate vendors—choosing tools that not only met technical needs but also reflected the county’s ethical standards.

To keep the policy relevant, they set up a working group to revisit and revise it as new risks and use cases emerge. And they made sure the policy was usable in practice—offering training, clear documentation, and day-to-day support to help staff apply it.

Conclusion

An AI governance framework ensures that the technology used by the local government reflects community values, meets legal and ethical standards, and supports day-to-day operations. A solid framework gives teams the clarity and authority to act—with purpose and accountability—at every stage: choosing vendors, protecting data, enabling transparency, and reducing bias.

King County proves it’s possible. By setting clear rules, coordinating implementation, and making policy part of daily work, they’ve shown how governance becomes a system—not just a document. That’s what transforms AI from a powerful tool into a true public asset.