Understanding AI Bias and Ethics in Intelligence Work

How much do you trust your AI?

As investigators, we’re taught to question our sources. To ask where a lead came from, who’s behind a tip, and what might be missing.

But what happens when the source is your AI model?

It suggests a match. Flags a link. Highlights a pattern.

And because it’s fast, precise, and “neutral,” we trust it.

That’s where bias can slip in unnoticed.

Bias isn’t just human

It can live in the data we train on, the way models are built, and the systems we use every day. In investigations, it can cause an operational risk. It affects which cases get prioritized, who gets flagged, and how resources are spent.

Some common examples:

• Models make assumptions based on incomplete or skewed inputs

• Feedback loops reinforce patterns that shouldn’t be there

• Human biases that were never reviewed or corrected can get absorbed into the model and influence future results

AI is extremely useful, but it doesn’t replace critical thinking. Analysts and investigators need to keep asking questions, challenge assumptions, and stay sharp.

Where it shows up in your work

We’ve been there... the work can be intense. Long shifts, urgent cases, endless streams of data.
AI can take some of that weight off your shoulders. It can be your go-to partner for cracking cases faster and seeing things more clearly. But like any partner, it needs guidance. And the right checks to make sure it does not lead you off course.

AI bias can surface in subtle ways, for example:

• Bringing up past facts that are irrelevant to the current case

• Flagging a suspect because of name similarity, without context

• Weighing social media activity without understanding platform norms

• Misinterpreting common behaviors in one region as anomalies in another

You could miss something important, or waste hours pursuing an irrelevant lead.

How to Use AI — Without Falling Into the Bias Trap

Here are the steps top teams use to keep AI reliable and effective:

Strategic steps:

Know your AI

• No matter what system you’re using, it’s important to know what’s under the hood.

  Understand the model. Know its strengths. Know where it might fall short.
  
  

Have an internal expert

• Assign someone on your team to be the AI lead. It could be any analyst who’s willing to go deeper and own that responsibility. It’s key to have someone who knows your specific AI model inside out, and can help the rest of the team make sense of it.

• Keep the feedback loop open. Your AI lead should connect regularly with the provider to troubleshoot, learn, and improve how the tool’s used.
  
  

Collaborate.

• Talk through your findings with others. Bias loses power when decisions are made as a team, not in a vacuum.
  
  

Track assumptions over time.

• As your investigation evolves, log what the AI is surfacing and compare it with what ends up being true. This helps you spot recurring gaps or blind spots in the model over time — and teaches your team what not to trust blindly.
  
  

Create a bias checklist.

• Formalize it. Just like you’d have a checklist for source validation, build one for AI-assisted analysis. Before acting on an AI insight, run through a short internal bias check:

• Is this based on multiple data points?

• Is this insight traceable?

• Does it confirm my initial theory too neatly?
  
  

Tactical steps:

Ask better questions.
When prompting the AI, be clear and specific — but don’t lead it.

• • • Give enough context so it knows what (and who) you’re talking about.

    • Be clear about what you’re trying to find, and what data it should use.

    • Avoid inserting your own guesses or assumptions into the prompt.

Example:
Instead of saying:
"Where does the target get his money from?"

Try:
"I’m investigating M. Johnson. I’m looking into his financial sources. Please review the provided bank transactions and map out any patterns or sources of income you find."

Avoid this:
"He’s probably laundering money through third-party accounts — let’s see which transactions prove that."
That kind of prompt can steer the AI toward your bias instead of the facts.

Look under the hood.

• • When the system surfaces an insight — a connection, a name, a pattern — take a second to ask how it got there.

    Was it based on keywords alone? Was there context? Was there overlapping info, such as identical names, that might have confused the AI model?

Don’t rely on one source.

• • Check what the AI says against field data, OSINT, and team input. If it only shows up in one place, question it.
    
    

A second set of eyes.

• • Ask a colleague for feedback on the AI’s conclusions.
    
    

Watch for “overconfident” language.

• • Some LLMs are trained to sound confident — even when they’re wrong. Be cautious of responses that feel certain but don’t show their sources or logic. Always ask for references and logical steps.