The Error We Trust

I used to believe that accuracy was accuracy, regardless of the source. A wrong answer from a human felt the same as a wrong answer from a computer. Therefore, I figured people would naturally prefer whichever system made fewer mistakes.

But I was wrong.

Last month, my GPS confidently directed me to turn right into a construction zone. As I sat there, blocked by orange cones and a very patient construction worker shaking his head, I found myself muttering, "Stupid GPS." But when my friend Sarah gave me directions to the same restaurant and accidentally sent me three blocks too far, I called her laughing. "Close enough! I found it."

Same mistake. Different reaction. But why?

The construction zone moment made me realize something unsettling about how we relate to errors. We don't just want accuracy. We want errors we can understand, forgive, and maybe even relate to.

Think about the last time a barista misspelled your name on a coffee cup. You probably smiled, took a photo, maybe shared it on social media. Now imagine an AI coffee-ordering system consistently misspelling names. That same charming error suddenly feels broken, incompetent, unacceptable.

The difference isn't in the mistake itself. It's in the story we tell ourselves about why it happened.

When Sarah gives me wrong directions, I assume she was distracted, misremembered, or was thinking about her own usual route. I can picture myself making the same mistake. Therefore, I forgive it easily, even find it endearing.

When my GPS fails, I don't imagine it having a rough morning or getting confused about one-way streets. I imagine faulty programming, insufficient data, or corporate corner-cutting. The error feels less like a mistake and more like a betrayal of the system's promise of perfection.

But here's where it gets interesting. AI systems are often more accurate than humans. Your phone's autocorrect catches thousands of typos you'd otherwise send. Medical AI can spot patterns in X-rays that experienced radiologists miss. Financial algorithms prevent fraud that human reviewers would overlook.

Yet when these systems fail, we feel differently burned.

A doctor who misses a diagnosis is having a bad day. An AI that misses the same diagnosis represents a fundamental flaw in artificial intelligence. A human accountant who makes an error is overworked. An automated system that makes the same error is "glitchy" and can't be trusted.

We're comfortable with human error because we understand its origins. We've all been tired, distracted, overwhelmed, or simply wrong. Human mistakes feel familiar because they mirror our own limitations.

AI errors feel alien because we can't empathize with how a machine fails. We don't understand why an AI suddenly thinks a chihuahua is a muffin or why it confidently states that Paris is in Italy. These failures don't map onto any human experience we recognize.

Therefore, we develop different relationships with different types of errors.

With humans, we assume good intentions and bad execution. With AI, we assume the execution is the intention. If the machine messes up, that's what it was designed to do, which feels more threatening than a person simply having an off day.

This creates a fascinating paradox. We often prefer the error-prone human over the more accurate machine, not despite the errors, but because of how those errors make us feel.

Consider your reaction to these scenarios:

A human translator who occasionally misses cultural nuances but captures the emotional tone of a conversation versus an AI translator that's more linguistically accurate but misses subtle human context.

A human financial advisor who sometimes forgets details but remembers your goals and values versus an AI system that optimizes returns but can't account for your fear of investing in certain industries.

A human customer service representative who gets flustered but genuinely wants to help versus a chatbot that efficiently resolves 90% of issues but fails spectacularly on edge cases.

In each case, the human error feels acceptable because we can imagine ourselves in their shoes. The AI error feels unacceptable because we can't.

But maybe our comfort with human error isn't always rational. Sometimes we're forgiving of human mistakes that we shouldn't be, while being unfairly harsh about AI mistakes that are actually quite reasonable.

A human pilot who's tired probably shouldn't be flying your plane, regardless of how relatable fatigue feels. An AI that occasionally misidentifies objects but never gets drowsy might actually be the safer choice, even if its errors feel more alien.

The question isn't really whether we prefer human error or AI error. The question is whether we're making decisions based on actual performance or based on which errors feel more comfortable to us.

As AI becomes more prevalent, we'll need to grapple with this discomfort. We'll need to distinguish between errors that matter and errors that just feel weird. We'll need to decide whether we want the mistake we can relate to or the mistake we can live with.

I still don't know which type of error I prefer. But I do know this: understanding why different errors feel different is the first step toward making better choices about when to trust humans, when to trust machines, and when to trust ourselves to tell the difference.

The next time you find yourself frustrated with an AI's mistake or charmed by a human's error, pause and ask yourself: Am I judging this fairly, or am I just more comfortable with the mistake I understand?

Your answer might surprise you.