Why AI Hallucinations Are a Governance Problem, Not a Tech Problem

If you work in public sector, you’ve likely heard a version of this by now:
AI makes things up.
AI hallucinates.
AI can’t be trusted.

These concerns aren’t overblown, but they’re often misunderstood.

Definitions vary, but in practice, "hallucination" is simple: the model confidently stated something that isn't true. Without grounding, constraining outputs to verified sources, that's essentially how LLMs work.

The issue isn't reckless technology. It's deploying AI without the grounding and accountability that public work requires.

Hallucinations are a technical reality. In the public sector:

They become a governance crisis.

The Public-Sector Risk is Different and Higher

In consumer or commercial settings, an incorrect AI-generated answer can be inconvenient or embarrassing.

In the public sector, it can be far more serious. A misleading statistic can undermine public trust. An untraceable insight can fail a Freedom of Information Act (FOIA) request. Sometimes the risk is simpler: a fast answer that no one can explain stalls decision-making entirely.

Public-sector teams don’t just need answers. They need answers they can stand behind, explain, and defend. That’s why speed alone is not the goal.

In public work, speed without defensibility is a liability.

Why do AI Hallucinations Happen? 

AI hallucinations don’t appear randomly. They are a predictable result of systems deployed without clear constraints, data boundaries, or accountability. Common causes include:

• AI generating responses from broad, unverified internet content
• Lack of grounding in structured, authoritative data
• No clear sourcing or citation of underlying datasets
• Outputs presented as final answers rather than decision support

When AI is asked to “fill in the gaps,” it often does convincingly, but incorrectly. This is especially dangerous in civic contexts, where accuracy and traceability matter more than novelty.

Reliability & Trust Must Be Earned 

Many conversations about AI safety focus on guardrails, filters, or post-hoc checks. Those matter, but they're insufficient.

Effective hallucination mitigation in public-sector AI requires treating it as an engineering discipline:

• Selecting models with the strongest baseline performance on grounding and factuality
• Designing systems that constrain the AI toward success paths rather than failure modes
• Grounding outputs in verified, curated data sources
• Testing and evaluating - rigorously, at scale before deployment
• Building clear UX for human oversight, auditing, and correction
• Applying government specific experience, use cases, and evaluations

This isn't about controlling AI through policy alone. It's about building systems that respect the realities of public accountability from the start.

What Does Trustworthy Civic AI Actually Require?

For AI to be usable in public-sector decision-making, it must meet a higher bar.

At minimum, trustworthy Civic AI requires:

• Verified data
  Insights must be grounded in authoritative, curated datasets, not scraped or inferred content.
• Transparent reasoning
  Users need to understand how an insight was formed, not just what the answer is.
• Clear sourcing
  Every conclusion should trace back to specific datasets, variables, and timeframes.
• Human-in-the-loop oversight
  AI outputs should support judgment, not replace it.
  

These principles are foundational to how mySidewalk designs its AI capabilities, which are grounded in a meticulously curated data library and built to support human decision-making, not replace it.

Why is Governance Also a Storytelling Problem?

Governance isn’t just about accuracy. It’s also about communication.

If an insight can’t be clearly explained:

• It can’t be defended publicly
• It won’t earn stakeholder buy-in
• It won’t survive scrutiny

This is where storytelling becomes a governance skill, not an afterthought.

AI systems that surface insights without context, narrative, or explanation increase risk, even when the underlying data is correct.

In public-sector work, credibility depends on clarity. How an insight is explained is often as important as the insight itself.

What This Means for Public-Sector Teams

Public-sector teams don’t have the luxury of “move fast and see what happens.” AI has to earn its place in decision-making.

It’s about building systems that can withstand audits, public records requests, and public scrutiny:

• Fewer black boxes
• More transparency
• Stronger data foundations
• Clear human accountability
  

When AI is designed with governance in mind, hallucinations shift from a looming threat to a solvable design challenge.

And when that happens, AI can help public-sector teams do what they’re trying to do every day:

Turn insight into decisions — responsibly, confidently, and in service of their communities.