Why is AI held to a higher standard than I am?
Originally published in the June 2025 issue of Magnet Unlocked. Want to be the first to see new content? Sign up for our monthly newsletter, Magnet Unlocked.
Years ago, I stood in a Colorado courtroom, arguing for the admissibility of a method I had developed to estimate vehicle speed using dashcam footage. The case was heartbreaking. A father riding a homemade motorcycle had been struck and killed by an impaired driver traveling at 105 mph. The only available evidence? The dashcam video from the offender’s own vehicle.
The footage was poor quality from an outdated camera. To reconstruct the scene, we reverse engineered the setup, replicated the field of view, and analyzed frame-by-frame data to estimate speed—using a technique never before presented in court.
That meant we had to pass a Daubert hearing; the legal standard in the US for admitting novel scientific evidence. The judge asked all the right questions: Had the method been peer-reviewed? Was it based on sound scientific principles? What was the error rate?
The method held up. The evidence was admitted; the defendant accepted a plea deal. Without proof of the extreme speed, the charges would have been far less severe. This forensic approach became central to the outcome of the case.
That experience has stayed with me, especially as I watch how artificial intelligence is treated in digital forensics. With AI, the standard suddenly shifts.
We insist on knowing every detail; what training data was used, what biases may exist, how the model’s internal mechanics function. Even when the output is accurate and useful, we hesitate.
It’s as if we’ve decided that AI must be flawless to be trustworthy, held to a standard that no human expert could ever meet.
The self-driving car fallacy
When a self-driving car crashes, public reaction is immediate and intense. But when a human driver causes the same accident, something far more common, it’s seen as unfortunate but expected.
Autonomous systems often outperform humans in safety metrics. They don’t get distracted, drunk, or tired. Yet we demand perfection from machines, while forgiving human error as inevitable.
This double standard extends to AI in digital forensics. A human examiner’s judgment is accepted with appropriate contextual questions. But when AI draws a conclusion from vast data and statistical models, we treat it with deep suspicion as if one error proves it can’t be trusted at all.
What makes a tool trustworthy?
Every time I testify in court, I’m asked about my credentials. That’s how the justice system gauges my credibility, through proxies of competence. But no one asks about the curriculum of my sophomore algorithms class, or the dataset used in a machine learning module I took in 2008.
So why do we ask that of AI?
If we accept that trust in human expertise can be built through qualifications and performance history, why can’t we do the same for AI tools? Why do we need to reverse-engineer a neural network’s every weight and parameter to believe what it shows us, when we don’t require the same of a seasoned examiner?
The question isn’t whether AI is flawless. The question is whether it meets the same reasonable evidentiary standards we already use for people.
Investigative lead, not final word
Most AI in digital forensic tools are not delivering conclusions, it is helping with triage. It points us to what might matter, not what it means.
If an intern flags an inappropriate message, I still read it and decide relevance. AI works the same way. It narrows the field, but the interpretation is mine. This reflects how teams already operate. We do not document every conversation that led to a discovery; we document the outcome.
We do not need to explain how we found the haystack if we are the ones who pulled out the needle.
Recalibrating the standard
AI needs oversight, but so do humans. Bias, fatigue, and gut instinct shape human decisions too.
So why is a hunch more trusted than a data driven inference?
The standard should not be perfection. It should be consistency and value. If AI helps reveal truth or speeds up investigations, it deserves fair consideration.
It does not need to be flawless. Just admissible. And often, it already is.
Authored by one of our experts, Brandon Epstein.
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