Guest Blogger: Stephen Coston

The views expressed are my own and do not represent those of my employer or any affiliated organization.

Courts Don’t Regulate Tools. They Regulate Testimony.

Every few months, the same question resurfaces in legal and forensic circles:

“How much should we trust AI?”

It sounds reasonable. Responsible, even.

And it’s also the wrong question.

Courts have never regulated trust.
They regulate testimony.

They regulate methodology.
They regulate who bears responsibility when an opinion is offered under oath.

What they have never done because they cannot is regulate cognition, intuition, pattern recognition, delegation, or exploratory thinking. That distinction matters more now than ever.

The Mistake We Keep Making About AI

Most of the anxiety around AI in forensics comes from a single category error:
confusing discovery with testimony.

We imagine AI as a kind of autonomous examiner:

• Drawing conclusions
• Resolving ambiguity
• Making decisions that courts must somehow evaluate

That picture is fiction.

In real forensic practice, AI when used properly does none of those things.

It reviews data.
It surfaces correlations.
It suggests places to look.

That’s it.

It does not testify.
It does not adopt conclusions.
It does not bear responsibility.

And once you understand that the entire “AI admissibility crisis” largely dissolves.

Courts Regulate Testimony, Not Thought

This is the core principle that grounds everything:

Courts do not regulate how conclusions are discovered.
They regulate how conclusions are justified, reconstructed, and defended.

Judges do not ask:

• How an expert first noticed a pattern
• What tool surfaced an investigative lead
• Whether intuition, automation, or delegation played a role

They ask:

• What evidence was relied upon
• Whether the methodology is reliable
• Whether the reasoning can be explained and reproduced
• Whether the expert is accountable for the opinion offered

That has always been the rule.

AI does not change it.

AI Is a Junior Analyst, Not a Witness
In practice, AI occupies a role the legal system already understands well. Senior examiners routinely:
• Delegate analysis to junior analysts
• Review investigator summaries
• Use scripts, spreadsheets, alerts, and filters
• Discard hypotheses that don’t survive verification
No one asks to cross-examine the junior analyst’s thought process. No one demands a “trust index” for Excel.
Why?
Because only the conclusions adopted by the testifying expert matter. AI, when used correctly, fits cleanly into that same category:
• It proposes
• The human expert disposes
• Responsibility remains singular and human
If an expert cannot defend a conclusion without referencing AI output, that conclusion should never reach testimony. And under a disciplined framework, it doesn’t.

The Line That Cannot Be Crossed
There is a bright line. And it is not subtle. The moment AI output is:
• Cited as reasoning
• Relied upon to resolve ambiguity
• Needed to explain or defend a conclusion
AI has crossed from discovery into methodology. At that point, the framework collapses by design.
That is not a loophole. It is an enforcement mechanism. This isn’t a safe harbor for lazy practice. It is a constraint system that punishes overreach. Bias, Explainability, and the Questions Courts Don’t Need to Answer
Once AI is structurally excluded from the evidentiary reasoning chain, a remarkable thing happens.

All the usual AI debates become legally irrelevant:
• Training data
• Model bias
• Explainability
• Error rates
• Version drift
Not because they don’t matter in general but because they don’t enter testimony. Courts do not need to evaluate tools that are not offered as evidence. They do not need to assess systems that do not supply opinions. They do not need to interrogate black boxes that never reach the witness stand. 

What they evaluate instead is something familiar:
• Preserved artifacts
• Verified logs
• Reconstructed timelines
• Human reasoning
• Human accountability
That is the quiet power of restraint.

Why This Is Actually Safer Than Traditional Practice
Here’s the uncomfortable truth:
AI-assisted discovery properly bounded is often less risky than accepted human practices.
Why?
Because it can be:
• Procedurally isolated
• Technically constrained
• Rendered fully discardable
Unlike undocumented intuition. Unlike hallway conversations. Unlike informal delegation. The framework doesn’t romanticize AI. It disciplines it.

What this Means for Investigators, Prosecutors, and Corporations
For law enforcement, this approach:
• Preserves due process
• Narrows Brady and Giglio risk
• Strengthens attribution defensibility

For corporate investigations, it:
• Avoids turning analytics into discoverable liabilities
• Preserves auditability without expanding disclosure
• Keeps decisions grounded in verifiable evidence

For courts, it does something even more important:
• It avoids forcing judges to become AI regulators

Instead, judges do what they’ve always done:
Evaluate testimony.
Evaluate methodology.
Evaluate accountability. 

The Future Is Coming and This Framework Knows Its Limits

This framework does not intend to solve every future problem.

It does not address:

• Autonomous AI
• Agentic systems
• Self-directing analytic decision engines

And that restraint is intentional.

When machines begin generating, selecting, or adopting conclusions without independent human reconstruction, new doctrine will be required.

Until then, clarity beats speculation.

The Final Word

AI does not testify.
AI does not form opinions.
AI does not bear responsibility.

The expert does.

And if we remember that clearly, consistently, and without fear the law already knows how to handle the rest.