Responsible Intelligence: Building Trustworthy AI Together

AI is moving faster than the policy systems designed to govern it. That tension, between the pace of technological change and the deliberate, durable nature of democratic governance, was at the center of the Responsible Intelligence panel at the Boise Metro Chamber's 2026 Regional Leadership Conference. With a lawyer, a legislator, and a city CIO around the table, the conversation was candid, practical, and at times unexpectedly funny.

Moderated by Amy Johnson, director of government affairs, marketing, and business development at Syringa Networks, the panel featured Tom Mortell, co-managing partner of Hawley Troxell; Representative Jeff Ehlers, a second-term Idaho House member who co-chairs the legislature's AI committee; and Alex Winkler, CIO for the City of Boise, who stepped in as a last-minute addition and delivered some of the most grounded insight of the day.

Federal Framework or State Control? The Answer Is Complicated.
Johnson opened with a foundational question: should AI regulation be driven by a strong federal framework, or should states retain the ability to shape their own rules? Representative Ehlers came down firmly on the side of state authority, at least for now. States, he argued, are laboratories of democracy. Fifty different approaches to a problem this new and this complex will surface better ideas faster than a single top-down framework ever could. Idaho has already been active at that level, and Ehlers noted that he reached out to Senator Risch's office to push back on federal efforts to preempt state action.

Mortell offered a counterweight. As a practicing attorney whose clients often operate across dozens of states, the prospect of navigating thirty different regulatory regimes is a genuine business problem. The principles of interstate commerce have a way of complicating what looks clean at the state level.

Winkler brought the municipal perspective. At the city level, the most immediate friction is not AI-specific policy but procurement. When vendors are responding to different rules in different states and municipalities, the complexity of buying responsibly multiplies. Her practical question: what can the City of Boise actually do comprehensively and safely given the tools and frameworks currently available?

Responsible AI Starts with Your Data, Not Your Policy Document
When Johnson asked what responsible AI actually looks like inside a real organization before the press releases and policy statements, Winkler did not reach for a framework. She reached for the basics.

Garbage in, garbage out. Every AI tool runs on data. How clean is that data? How well is it classified? Does the organization even know what it has and where it lives? Winkler described the data landscape at the City of Boise in terms she knows well from her two decades in the Air Force: some of it is essentially top secret, protected under PCI, HIPAA, and criminal justice information standards. Some of it is entirely public. Knowing which is which, and making sure AI systems only touch what they are authorized to touch, is the foundational work that has to happen before any tool goes live.

She identified three areas of accountability that frame how the city approaches every AI deployment: how it is procured, how it is configured, and how it is used. Each layer carries its own risk surface and requires its own set of controls. Access controls, audit trails, data encryption, data segregation, and secure coding practices are not AI-specific concerns. They are technology fundamentals that apply here just as they do everywhere else.

She also noted that AI has a way of making organizations confront data hygiene problems they have been able to ignore. When you shine a light on your data by putting an AI system in front of it, you will either fix the problems fast or want to hide them again. Either way, you will know the truth.

When Something Goes Wrong, Who Is Responsible?
The accountability conversation was one of the most substantive of the panel. Mortell laid out the legal reality bluntly: AI developers have written their contracts to protect themselves. Indemnity obligations are disclaimed. Liability is capped or eliminated entirely. The developer has the best defenses. Everyone else, the organizations deploying AI, the employees using it, the businesses building products on top of it, is left holding the bag when something goes wrong.

He described two areas of his own practice where this plays out with high stakes. At his law firm, the concern is attorney-client privilege: does putting client information into an AI system create a waiver that a court would recognize? In healthcare, it is HIPAA: does using AI to analyze patient records in ways that were not fully vetted create protected health information exposure? Good institutional policy and consistent employee education can reduce the chances of mistakes, but when mistakes happen and they will, the developers are protected. The rest of the chain is not.

Ehlers raised the human accountability principle that guides his work on the legislature's AI committee. A judge in another state used AI to determine criminal sentences, reasoning that the tool would be less biased than a human. It turned out to be more biased. Ehlers's view: the judge is still accountable. Using AI as a tool does not transfer responsibility to the tool. A tax appeal decided by a black box with no human in the loop is not something the public should have to accept. That principle, that a human must remain accountable for every consequential AI-driven decision, runs through the Idaho legislature's approach to the issue.

Winkler was characteristically direct on behalf of local government: if the city uses AI, the city owns the outcome. No contract is going to shift that liability. So the city's job is to procure carefully, configure thoughtfully, and train people to use these tools in ways the city can stand behind.

Innovation vs. Guardrails: Nobody Has Figured This Out Yet
Johnson pressed the panel on where the line sits between enabling innovation and putting guardrails in place, and who gets to draw it. Ehlers gave an answer that was refreshingly honest: nobody has figured it out. The legislature reflects the same spectrum of opinion that exists in the public, ranging from people who wish AI had never been invented to those who think the market should run entirely free. Without public consensus, meaningful legislation is difficult to pass.

One area where consensus did form: AI in K-12 education. A framework for how AI should be used in Idaho schools passed with broad bipartisan support, with Ehlers as the House sponsor. It was, he said, an example of what becomes possible when there is enough shared concern to find common ground.

Mortell pointed to two practical approaches that can coexist. Disclosure obligations are relatively manageable: requiring developers to warn users that outputs can be wrong, that the tool has limitations, and that the buyer should verify. Use-based regulation is harder. He described the federal approach to AI in electronic health records, where regulators are requiring that AI-generated clinical prompts be linked to the underlying studies so physicians can evaluate the evidence rather than simply accepting the suggestion. That level of specificity takes time and expertise to develop.

For Winkler, the innovation-versus-guardrails tension is not a policy abstraction. It is her job description. Her role is both to enable city services through technology and to protect those services and the data behind them. Every decision involves weighing both sides simultaneously. She framed it as applied risk management, the same discipline, applied to a new category of tool.

AI Is Already on the House Floor
One of the most vivid moments of the panel came when Ehlers described how AI has changed the experience of legislating in real time. During the last session, a colleague used a free AI tool to evaluate one of his bills. The tool pulled from Facebook and other sources, surfaced conspiracy theories, and produced a negative assessment that nearly derailed the legislation. Ehlers responded by finding an Idaho-specific AI tool trained only on Idaho law and regulation, running the same bill through it, and using that output to make the case to his colleague directly.

The experience changed how he thinks about floor debate. When he rises to speak on a bill, he is no longer just addressing the seventy members of the House. He is addressing those members plus the AI tools many of them have running in parallel, fact-checking his statements, feeding the bill through their preferred models, and generating counterarguments in real time. He estimated that perhaps twenty of the seventy members had AI tools running during the last session. He expects that number to be closer to forty or fifty by next year.

The panel closed with a question that had no easy answer: looking five years ahead, what matters most in building responsible AI? Good regulation, strong public trust, or bold innovative leadership? Mortell made a case for the lawyers. Ehlers chose innovation, arguing that good policy follows good progress, not the other way around. Winkler chose public trust, reflecting the obligation she carries as a steward of city data and city services.

It was, as Johnson noted in closing, a fitting end to a conversation where the honest answer was probably all three. The work ahead is figuring out how to build them together.
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Tom Mortell, Representative Jeff Ehlers, and Alex Winkler delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 20, 2026. This blog post was prepared from a partial transcript using the help of AI.

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