Fire Investigation in the Age of AI: Why Human Judgment Still Matters

Colleagues in fire investigation keep talking about AI. Whether it be se reports, cross-check conclusions against NFPA 921 and other recognised texts, and even verify terminology from authorities like Kirk’s Fire Investigation or The Ignition Handbook. The appeal is obvious: busy investigators can lean on AI to surface relevant research quickly, highlight inconsistencies in draft reports, and suggest alternative hypotheses that might not have been considered, saving hours of tedious work.

Yet this tooling introduces new quandaries. Bias already lurks in every assumption investigators make: expectation, confirmation, or anchoring bias, or the pull of previous experiences or cultural conditioning. NFPA 921 explicitly warns that unless rigorously kept in check, these human biases can skew an investigation’s direction. And now AI, with its own set of biases, is adding a hidden and insidious layer. AI promises speed, consistency, and scale, but it also introduces blind spots, including training data bias, algorithmic bias, sampling bias, and misinterpretation inherent in the model.

Consider human bias: you walk into a burned kitchen and immediately think “unattended cooking,” leading you to discount signs of faulty wiring unintentionally. Expectation bias, confirmation bias, and anchoring bias are shortcuts in thinking that can derail an objective analysis unless actively challenged.

Now consider AI bias: if a model has been trained primarily on urban structure fires, its suggestions may default to electrical failure, even when assessing a wildfire-wildland interface fire. AI bias is systematic, baked into how the model was designed and trained, and far more predictable in its misdirection.

What’s striking is how differently human and AI biases behave. Human bias is complex, variable, and emotionally influenced by factors such as fatigue, ego, expectations, or prior experiences. AI bias, by contrast, is uniform and insidious; investigators may trust AI conclusions just because they seem systematic or “objective,” when in fact they may reflect overrepresented case types, flawed training data, or algorithms prioritising superficial features over nuance. Ignoring either kind of bias can be dangerous: misidentifying the fire’s origin, misattributing its cause, damaging credibility in court, or even institutionalising flawed investigative practices.

AI also raises ethical and procedural concerns. Even when used for compliance purposes, such as aligning report content with NFPA 921 or NFPA 1033, or structuring scene examination narratives, AI outputs must be critically reviewed and validated. They may improve efficiency, but they cannot replace human judgment. The investigator, ultimately, must stand behind what goes into a final report, with clear awareness of what was AI-assisted and what was his or her own reasoning.

That means onboarding AI thoughtfully, using it to supplement, not supplant, investigative rigour. It’s the investigator’s responsibility to mentor newer colleagues on this: AI can flag potential gaps in a narrative, highlight inconsistencies, or help format evidence logs, but it can't evaluate scene context, weigh competing hypotheses with human experience, or testify in court. It’s our role to teach them how to audit AI outputs, to cross-reference them against physical evidence, research, and accepted fire science.

For investigators who came into the field long before AI emerged, the distinction is clear: fire investigation is not just about pulling quotes or polishing prose, it’s about scene examination, hypothesis development, nuanced interpretation of fire dynamics, and scientific discipline. No AI can replicate walking through a burned structure, sensing subtle patterns or shifting lines of reasoning in real time. AI can serve as a supporting tool, an accelerator for drafting, summarising, or organising, but never as a replacement for investigative expertise.

As AI continues to evolve, we must stay grounded in mentorship, peer review, and structured scrutiny. This means reviewing AI outputs with suspicion, checking them against NFPA 921’s step-by-step methodology and NFPA 1033’s standards for investigator qualification, and being transparent about what was AI-assisted. We must guard our findings against both human and machine bias, ensuring that our final reports remain grounded or organised in disciplined, defensible science.

The credibility of a fire investigation rests not in technology, but in the judgment, integrity, and experience of the investigator.

AI can assist, but it cannot investigate, nor can it replace us.