Should companies use machine learning for employee performance reviews or stick to human judgment? | AI Insights | Lumman.ai

Let’s get something out of the way: performance reviews aren’t sacred. They’re a mess.

We act like they’re these sober, reflective rituals done in laminated conference rooms by wise managers. In reality? They’re bias soup. One part “who’s loud in meetings,” a dash of “who reminds me of younger me,” and a generous pour of “what have you done for me lately.”

But now we want to sprinkle machine learning into this mess. Not just to speed it up—but to make it “fair.”

And that idea? It’s more dangerous than it sounds.

The False Promise of Algorithmic Objectivity

Let’s say you train a machine learning model on your company’s past performance data.

Sounds smart, right? Use the patterns of success to flag high-potential employees, spot underperformers early, and banish gut-based decisions.

Except...

What if your company's definition of “success” was broken to begin with? What if the system favored extroverts who volunteer for visible projects, or employees willing to answer Slack messages at 10 p.m.? What if it punished anyone who prioritizes deep work over performative busyness?

Feed that into the model, and congratulations—you’ve just automated your old bad habits. With graphs.

Remember Amazon’s notorious recruiting algorithm? Trained on ten years of hiring data, it learned that “women’s chess club” meant “not a good hire.” It didn’t invent that bias—it inherited it. That’s how ML works. It recognizes patterns, even if those patterns are quietly sexist, ableist, or just dumb.

But here’s the kicker: now that bias comes dressed up as objectivity. “Sorry Priya, your performance rating dropped according to the system.” As if “the system” wasn’t just repeating the preferences of your loudest, bro-iest regional director from 2016.

Humans Are Biased, But Machines Are Complicit

“But wait,” you might say, “humans are biased too!” Yes. Undeniably.

Managers forget accomplishments, reward the people they enjoy working with, and harbor unconscious biases that tilt reviews without anyone noticing.

Nobody’s defending the status quo. That’s not the point.

The point is this: replacing flawed humans with flawed machines doesn’t eliminate bias. It just makes it harder to see. With a manager, at least you can push back. You can ask why. You can challenge the logic.

Try that with a black-box model trained on Jira logins and calendar invites.

I once saw a case where an algorithm flagged a developer as underperforming because their code commits dropped 40% over a quarter. The truth? They were mentoring three new hires—helping them ramp up, fixing their bugs, and basically keeping the team afloat. No dashboard could see that. But everyone on the team could feel it.

When AI Becomes the Judge Instead of the Analyst

Machine learning is good at spotting patterns. Humans are (sometimes) good at interpreting context. The mistake is thinking you have to choose.

Here’s what bad looks like: using ML to auto-generate performance scores, auto-suggest promotions, or automate firing decisions based solely on metrics you barely understand.

Worse still? Hiding behind it.

“Sorry, the algorithm says you’re underperforming.” That’s not efficiency. That’s cowardice in drag.

Good looks more like this: the model flags anomalies or blind spots. Maybe it notices that one manager always scores women lower on “strategic thinking.” Maybe it surfaces an employee whose long-term peer feedback is stellar, despite being overlooked year after year.

Then the human—ideally one with emotional intelligence and a spine—steps in to figure out what’s actually going on.

This isn't a question of automation. It's a question of judgment. Who owns the decision, and who gets to appeal it?

When the ML tool is the analyst, not the judge, you’re on solid ground.

The Feedback Loop You Didn’t Know You Created

Here’s the part that should bother you most: data doesn’t just reflect what’s happening. It shapes what happens next.

If employees figure out the algorithm values response time to emails, is that what they’ll optimize for? If it tracks meeting participation, do you end up rewarding interrupters and punishing quiet thinkers?

Once people know the inputs, watch them dance to the tune. That might not feel like a problem—until your culture becomes performative, anxiety-ridden, and feels like competing in a digital Hunger Games.

A performance system that codifies shallow metrics without critical oversight doesn’t just evaluate your employees—it trains them.

Better Questions, Better Systems

If you really want to fix performance reviews, start by asking questions that hurt a little.

What do we actually value: visibility or impact?
Who consistently gets high ratings, and why?
How do we define “collaboration,” and is it more than just being liked by people with power?

Then, let the machine show you where your answers don’t match your behavior.

Let it surface trends. Let it challenge suspicious patterns. Let it nag you when your team’s reviews have inexplicable gender or racial skews.

But don’t let it make the final decision. That’s your job.

Because if you’re going to tell someone they didn’t make partner or they’re not on the high-potential radar, you’d better be damn sure you can explain why. In English. Not math.

What It Looks Like When You Get It Right

There’s hope.

Some companies are doing this well—not by going full robo-boss, but by designing co-pilot systems.

One tech company used ML to flag when managers consistently overrated people with high in-office hours, regardless of output. It was an uncomfortable revelation—but it forced a reckoning about what they truly valued.
Another used a model to find “quiet outperformers”—employees with exceptional peer reviews and long-term output who weren’t getting noticed. They called it “talent oxygen”—the people the organization didn’t know it was suffocating.
A third used ML to create alerts when promotion nominations heavily skewed toward a single demographic. Not to block them—just to pause and ask, “Are we sure?”

Those are all examples of augmentation, not automation.

You don’t remove the human. You sharpen them.

Here’s What Business Leaders Need to Stop Pretending

There’s no such thing as neutral data. If your history is biased, your model will be too. And it’ll look objective doing it.
Human judgment isn’t sacred. But it’s interpretable. And that counts for something.
If you can’t explain why someone got the rating they did, you shouldn’t be rating them. Algorithmic opacity is not a strategy.
Good performance review systems aren’t cheap or easy. They require ruthless honesty about what you measure, what you reward, and which signals truly reflect value.

If your goal is to save time or avoid difficult conversations, machine learning isn’t your fix. That’s just outsourcing responsibility to the quietest person in the room: your model.

But if your goal is to build a better process—more reflective, more aware, more accountable—then ML has a role to play. Just not the starring one.

Final Thought: Build Systems That Make You Better, Not Dumber

Performance reviews are where culture becomes policy. They’re how you signal what matters and who matters. They don't just evaluate people—they teach them what to become.

So ask yourself: do you really want to turn that over to a system that’s been trained on all your company’s old ghosts?

Or do you want help seeing more clearly, so you can own the decisions that shape your future?

Because this isn’t about replacing flawed humans with flawed machines.

It’s about building systems where what matters most doesn’t get missed—by either.