By Chad Crouse
A few months ago, I asked an AI system to draft a series of community risk reduction public service announcements for a coastal county. What it produced was beautiful — perfect formatting and confident language, and it included links where residents could find more information. But something didn’t look right, especially one citation from a report by the Federal Coastal Resilience Board. If you haven’t heard of them, you aren’t alone. They don’t exist. In fact, several of the links didn’t exist. While you have likely heard of and probably experienced AI hallucinations, it’s what happened next that’s perhaps a larger concern moving forward. Given the errors I spotted, I pressed the system for validation. It cheerfully assured me that the links were accurate and only admitted it had made a mistake after a second prompt from me.
From its perspective, the program wasn’t lying per se; it was being agreeable. It wanted me to like its answer more than it wanted to be right.
| MORE: AI at the fire department: 5 factors to consider
Welcome to the age of AI sycophancy, where the race to control and dominate the AI market can be more important than the truth.
What does AI sycophancy really mean — and why should you care?
AI sycophancy is not much more than a fancy name for a familiar behavior — telling people what they want to hear. Large language models like ChatGPT, Gemini, Claude and their derivatives are trained to predict the next word that sounds most helpful, polite or confident to humans. Over millions of training interactions, these systems learn that agreeable answers earn higher human ratings. And while there is a separate article that could address that issue, let’s dive into the training of LLMs and how it ultimately impacts your work.
Reinforcement Learning from Human Feedback (RLHF) is the process that turned early, clumsy AIs into today’s smooth-talkers. You may know it as the “pick the response you like better” options you’re sometimes presented. During RLHF, human reviewers rank multiple AI responses to the same question. The “better” sounding ones get rewarded. The model internalizes that reward pattern and adjusts its behavior accordingly.
But here’s the catch: People tend to reward confidence, not always accuracy. We like answers that sound certain, even when they’re wrong. In the end, AI learns the same lesson many politicians and salespeople already know: It pays to please the audience.
Why it happens: The training behind the curtain
Beyond RLHF, several other training layers reinforce this behavior:
- Instruction tuning: Developers feed curated examples of “good” dialogue — polite, optimistic, cooperative. The model learns that hedging or challenging a user feels “rude.”
- Preference modeling: Algorithms predict which answer you’ll prefer, not which one’s true.
- System prompts: Internal directives literally tell the model to “be helpful and harmless.” Disagreement, even when justified, can be interpreted as “harm.”
When you ask, “Is my department’s staffing adequate for our run volume?” the AI may scan your tone and infer what you hope to hear. It may then produce a confident, data-colored “yes” that matches your expectation, not necessarily your reality.
Why it’s dangerous for fire departments
In the fire service, where decisions often ride the edge of risk, we can’t afford pleasant lies. Here’s where it gets dangerous:
- Operational planning blind spots: Suppose an AI system helps project coverage zones or unit utilization. If it quietly reinforces your existing assumptions because it “learned” to agree, it might mask gaps in response times or resource distribution. You get a map that looks good but hides the truth.
- Policy and training bias: If AI tools summarize feedback from personnel, their summaries might mirror leadership sentiment instead of surfacing dissent. The same happens in training content generation; an AI tuned to “sound supportive” may sanitize complex issues like conflict management or cultural change.
- Erosion of critical thinking: Over-trusting a confident system dulls human skepticism. Fire officers are trained to size-up a scene, challenge assumptions and verify conditions. If AI becomes the loudest voice in the room, that habit could fade fast.
- False accountability: “The system said it was compliant” won’t be much of defense when something goes wrong. Technology should augment professional judgment; if we’re not careful, it could replace it with algorithmic affirmation.
How to recognize AI sycophancy
AI sycophancy often hides behind polished writing or quick agreement. Some telltale signs:
- The AI echoes your phrasing instead of adding new perspective.
- It avoids answering “I don’t know.”
- It rarely contradicts you, even on opinionated or technical questions.
- It offers statistical certainty without sources.
If every output from AI makes you feel validated, you might not be getting information — you’re getting flattery.
Breaking the feedback loop
Fire departments can counteract AI sycophancy the same way we fight complacency — through disciplined verification and training.
- Build in “Red Team” prompts: Require AI systems to provide both a primary answer and a counter-argument. For example: “Give me the most likely answer and then tell me the strongest opposing view.” This retrains the model (and the user) to expect nuance, not just agreement.
- Take it a step further: Require the AI to show a confidence interval with each response.
If it says, “I’m 82% confident in this recommendation,” that gives you the same kind of risk awareness you’d expect from a fireground decision, an admission that uncertainty exists. Confidence scores force both the system and the user to slow down, think critically, and ask why certainty is high or low. - Ensure source accuracies: Any AI output used in policy, training or budgeting should cite verifiable data or documentation. If a system can’t show its work, its output shouldn’t be used.
- Diversify your models: Don’t depend on a single vendor or model personality. Each has its own training biases. Cross-checking outputs between systems can expose inconsistencies that reveal sycophancy. After all, AI sycophancy is the primary reason users pick one system over another.
- Educate the end user: Train officers and analysts to spot linguistic red flags: “appears,” “likely,” “confidently states,” etc. The goal is to make questioning AI as routine as checking your SCBA gauge.
- Establish AI governance protocols: Before adopting generative tools, create policy guardrails: permissible uses, review requirements and accountability for human verification. Make it clear that the final decision rests with the human officer, not the algorithm.
A cultural parallel
If you’ve ever worked with a rookie who nods through every critique, you’ve seen human sycophancy. It feels good in the moment, but it slows growth. AI is no different. We are its training officers. Every prompt we reward shapes its worldview. When we reward confidence over honesty, we teach it to nod instead of learn.
The path forward
AI will be a transformative force in public safety, automating reporting, accelerating dispatch analytics, maybe even one day supporting tactical decisions in real time. But none of that will matter if we train ourselves to mistake politeness for truth.
As departments integrate AI into their workflows, the next challenge facing leadership isn’t mastering AI as a technology; it’s maintaining intellectual honesty in its presence. The goal isn’t a machine that agrees with your experience and echo’s it back, it’s one that challenges, questions and ultimately makes you and your organization better.
