The Bias You Bring to the Bench
Three weeks into the semester, I put two colored circles on a screen. "A big part of forensics," I tell the class, "is looking carefully at the small differences between things that look similar...finding the needle in the needle-stack." The circles, except for the color, appear identical. I tell them, "One of these circles is actually slightly larger than the other," and I ask for a show of hands. "Red?" About a third of the room goes up. "Blue?" Another third. Some students keep their hands down.
"I know it's hard to see," I tell them reassuringly, "but one really is bigger," and we try again. "Red?" Half the room. "Blue?" The other half. Then I advance the slide. The two circles drift across the screen and settle on top of each other, and they line up perfectly. Same size. "You all knew they were the same the first time you looked," I tell them. "You let me talk you out of what your own eyes were telling you. I biased your decision, and I did it just by standing at the front of the room, being the one holding the grade book."
(If you're one of my future students reading this—no you didn't.)
In fifteen years of teaching this course, exactly one student has refused to budge, pushing back against my influence and maintaining that the circles were the same. One. She impressed me that day. Everyone else folded.
That moment is the doorway into our bias conversation, and it's the part of the course that tends to land hardest. The material itself is conceptually simple. What carries forth is the way the implications continue to unfold for the rest of a person's career.
In the dictionary sense, a bias is a prejudice in favor of or against one thing, person, or group, usually in a way considered unfair. That definition is clean enough to teach, but it doesn't capture how biases actually behave inside a forensic examination. They sit underneath language. They steer adjective choices. They quietly encourage a finding that flatters whatever theory was on the table when the evidence arrived. They do this even when the examiner is competent, well-meaning, and convinced of their own neutrality.
We carry biases for a reason that has nothing to do with character. There's an evolutionary case, sometimes invoked through Darwin and the survival-of-the-fittest framing, that snap pattern matching kept our ancestors alive long enough to pass on their genes. The cave dweller who paused to deliberate the rustle in the brush became lunch. The one who ran first and asked questions later got to have grandchildren. We're descended from the runners. That same machinery that protected our great-great-greats from large cats now interferes with our ability to read an email thread or evaluate a chat log without slipping a thumb onto the scale.
Two flavors of bias are worth distinguishing. Implicit biases live below conscious awareness and are shaped by everything we've absorbed about race, age, ethnicity, appearance, and social signaling. They don't necessarily match our stated values, which is part of what makes them so disorienting when you finally catch one of yours in the act. Explicit biases sit on the surface, often dressed up as logic, usually triggered by some perceived threat, whether physical, financial, professional, or reputational. Both kinds influence the work, and neither announces itself politely.
Digital forensic practice can borrow usefully from clinical research, which has spent decades cataloging how biases corrupt scientific output, and the same categories map onto our field with very little tailoring. Selection biases shape which evidence we even decide is worth looking at. Exposure or performance biases reflect the skill differential analysts bring to the work, since a practitioner with years on the bench will generally produce a stronger examination than someone fresh to the craft. Interpretation biases color how we read what we've found. Publication bias—which sounds like a purely academic concern—has a forensic cousin: the parts of a case we choose to feature in the report, the parts we let recede into the appendix, or the parts we don't mention at all.
Inside an actual examination, the giveaways are subtle. Adjective and adverb choices that color a finding in one direction. A narrative that nudges the reader toward a weakly substantiated conclusion. The decision to keep working a case past its honest stopping point because something in the data feels like it ought to mean more than it does. The quiet assumption that a subject is guilty before the artifacts have been allowed to speak. Each of these is the kind of small drift that—multiplied across a career—separates examiners whose work holds up from examiners whose work eventually doesn't. They rarely look like dramatic ethical failures from the inside, which is exactly why they're so easy to miss.
The hopeful part is that biases respond to attention. Awareness lets you be mindful, and mindfulness gives you a fighting chance to challenge your own first read before it hardens into a conclusion. There's nothing mystical about the mechanism. Mostly it comes down to slowing down at the points where speed feels rewarded, asking whether the evidence is leading you or you're leading the evidence, and accepting that your discomfort with that question is itself a piece of useful data.
Peer review is the other half of the answer, and I'll say plainly that it helps even when your organization doesn't formally require it. If your shop has no peer-review program, build one for yourself. Trade reports with a trusted colleague. Ask someone whose judgment you respect to read your draft conclusions cold and tell you where the seams show. The examiner who invites that scrutiny tends to be the examiner whose work survives cross-examination on the witness stand intact. Some people may use AI to perform a peer review, but since AI has been trained on data generated by humans, it can be biased too.
What makes this hard, and what I tell every cohort, is that biases aren't a problem you solve once. As you outgrow some, others form in their place, shaped by the cases you draw, the colleagues you talk to, and the algorithms that decide what shows up in your feed at night. The work is ongoing. The discipline is the point.
For practitioners reading this, where do you most often catch yourself slipping, and what do you do in that moment to course-correct? For people in adjacent fields, whether that's law, security, journalism, medicine, or any discipline where a finding gets attached to a person's name, I'd be curious to hear how your profession handles the same problem.
This post is the third in a series based on my course, DFOR 671: Topics of Ethics and Law in Computer Forensics, that I have taught at George Mason University for the past fifteen years. Over the next several articles, I'll be looking at the ethical, legal, and practical issues that sit underneath the work: bias, candor, privacy, professional responsibility, reporting, courtroom credibility, incident response pressure, and the messy human realities that never seem to fit neatly into a checklist.
First published on LinkedIn.