AI as Judge: Should Algorithms Determine Your Next Promotion?
Look, I get the whole "AI as co-founder" framing. It's seductive to think we can just plug in algorithms and get leadership-level insights without the ego battles and vacation requests. But there's something deeply problematic about applying this thinking to performance reviews.
When we talk about "ruthless" decision-making in evaluating humans, we're not just being efficient—we're abdicating responsibility. Performance reviews aren't just data problems; they're human conversations happening within power structures.
Remember Microsoft's experiment with productivity monitoring? They built a system that tracked keyboard activity and meeting attendance, only to create what employees called a "workplace surveillance system." The technology wasn't wrong—it accurately measured what it was told to measure. But it optimized for the wrong things.
The question isn't whether AI should be in the room during performance reviews. It's about who trained it, what data it prioritizes, and most importantly, who takes responsibility for its conclusions. An algorithm trained on past performance data will inevitably encode the biases of previous managers, making today's inequities tomorrow's "objective metrics."
I'm not saying keep AI out of HR. I'm saying if you're going to let it in, make it the analyst, not the judge. Use it to surface patterns a human might miss, but have the courage to put your name on the final assessment.
Let’s not kid ourselves—human judgment in performance reviews is far from pure or objective. Bias, favoritism, recency effect, halo effect—you name it. We humans are walking bags of cognitive distortion. So the idea that manual reviews are this trusted gold standard? That’s a pretty generous myth.
But before we start throwing ML at the problem like it’s magical fairness dust, let’s be clear about something: machine learning isn’t inherently less biased. It’s just differently biased. If your training data comes from a historically biased process, congratulations—you’ve now automated those same flaws at scale, with the added bonus of making them harder to detect.
Take Amazon’s infamous hiring algorithm that downgraded resumes with the word “women’s” in them. That wasn’t some rogue code—it was faithfully learning from historical patterns. And that’s kind of the point. If the underlying system is broken, ML may just be a very efficient way of calcifying bad habits.
So no, I wouldn't blindly trust a machine to tell me who’s high-potential and who isn’t. But I also wouldn’t trust a manager who can’t remember what their direct report did three months ago. The real question is: can ML be used *with* human judgment, not instead of it?
For example: Imagine a system that flags discrepancies between manager ratings and peer feedback, or highlights patterns over time that might otherwise be lost in the 'it's been a busy quarter' fog. That’s value. But only if we treat it like a second opinion, not gospel.
The mistake is thinking this is a binary choice—machine or human. It’s not. It’s about which problems each is good at solving. Machines are great at pattern recognition, bad at context. Humans are decent at context, terrible at consistency. So why not build reviews that leverage both?
But if a company is using ML just to make reviews faster or cheaper without thinking hard about data integrity, feedback loops, or accountability? That’s not innovation. That’s abdication.
Look, I'll be blunt - businesses treating AI like a glorified Excel macro are playing a dangerous game of digital nostalgia.
When it comes to performance reviews, we're weighing two deeply flawed systems against each other. Human managers bring emotional intelligence but also carry every cognitive bias in the psychological textbook. They play favorites, remember that project you bombed six months ago, and conveniently forget how you saved their bacon last quarter.
Machine learning doesn't have these emotional blindspots, but it has others. It amplifies patterns in your data - including your prejudices - with ruthless mathematical precision.
The mistake is framing this as an either/or question. The real innovation happens at the intersection: AI flagging patterns humans miss, humans providing context AI can't grasp. Amazon tried building an AI hiring tool that ended up penalizing résumés containing the word "women's" because their historical hiring data was male-dominated. The solution wasn't abandoning the technology - it was recognizing its limitations.
What keeps me up at night isn't companies using AI for reviews - it's companies implementing these systems without understanding how they work. If you can't explain why your algorithm flagged an employee as underperforming, you shouldn't be using it to determine their career trajectory.
So should you use ML for performance reviews? Only if you're ready to put in the hard work of teaching it your values, not just your metrics.
I get the appeal. Machine learning promises objectivity. No favoritism, no forgetfulness, no “Oh, they’re just great in meetings” as a justification for a promotion. But here’s the catch: ML models are just pattern recognizers. They learn from historical data—which, in most companies, is soaked in subjectivity and bias. So unless your training dataset was curated by angels, you’re just automating your existing flaws.
Take Amazon’s infamous recruiting algorithm. It learned from a decade of hiring decisions and decided, “Ah, I see—men are preferable,” and started penalizing resumes that included the word “women’s,” like in “women’s chess club captain.” That wasn’t ML misfiring. That was ML doing exactly what it was trained to do: emulate past patterns.
Now apply that to performance reviews. Say your leadership historically undervalued the contributions of employees who weren’t loud in meetings or didn’t volunteer for overtime. A model trained on past ratings will pick up on that and bake it into the future. Congratulations, you’ve made the bias scalable.
And then there's the question of context. ML can track metrics, sure—quota hit rate, code commits, ticket velocity. But it can’t see that Priya spent half her quarter mentoring a floundering teammate back to competence, or that Miguel resolved an inter-department cold war that the CEO never even noticed. Those things don’t show up in dashboards—but they’re the glue that actually holds a team together.
So I’m not saying don’t use ML at all. Sprinkle it in. Use it to flag outliers, spot long-term trends, maybe surface patterns a manager might miss. But the final call should still be human—ideally, a well-trained, empathetic one capable of reading the unwritten parts of performance. Otherwise, we’re not automating fairness. We’re just wrapping old biases in new software.
Look, we've been pretending human-only performance reviews are some sacred, objective process when they're actually riddled with bias, recency effects, and politics. The manager who likes your jokes rates you higher than the one who doesn't. That's just reality.
But swinging completely to ML-driven reviews? That's trading one flawed system for another. These algorithms will faithfully reproduce and amplify every bias in your historical data. Congratulations, you've automated discrimination!
The sweet spot is obvious but uncomfortable: augmented judgment. Let the algorithms flag patterns humans miss – like identifying the quiet performers whose contributions go unnoticed because they don't self-promote. Use ML to challenge managers when their ratings show statistical anomalies across gender or race.
What makes this tricky isn't the technology but the transparency. Most companies want AI as a black box they can blame decisions on. "Sorry, the algorithm says you're underperforming." That's organizational cowardice masquerading as innovation.
Real leadership means using these tools while owning the decisions they inform. The companies getting this right aren't asking if they should use ML in reviews, but how to combine machine intelligence with human judgment in ways that make both better than either alone.
Sure, but here’s the part people aren’t talking about enough: machine learning systems aren’t neutral observers—they’re parrots trained on messy human data.
If you’re using ML to review performance, that model is learning from past "successful" employees. But what if that success was biased—favoring loud contributors over quiet ones, or overvaluing busywork that looks impressive but has low impact?
Let’s say your sales org historically promoted the aggressive closers—the ones who hit quota but torched long-term client relationships. Train a model on that, and you’re basically baking short-sightedness into the system. Now the quiet account manager who spends months building trust? Downgraded by the algorithm. Oops.
And the worst part? Unlike a flawed human manager, the machine hides its logic. At least when a VP gives a nonsensical review, you can call B.S. and ask them to explain. Good luck asking XGBoost why Sally scored a 3.2 instead of a 4.1.
So no—we can’t pretend machine learning is some magically objective way to evaluate performance. It’s just formalizing all the bad habits we already had, at scale. Unless companies are prepared to rip apart the assumptions they're training on—and really sweat the definition of “good performance”—they’re just automating bias with better UX.
Let's be honest - there's something deeply uncomfortable about handing employee evaluations to an algorithm. But maybe that discomfort is precisely what we should be examining.
Human judgment in performance reviews is demonstrably flawed. We favor people who remind us of ourselves, we overvalue recent performance, and we're terrible at separating personality from productivity. At least the machine won't play favorites because someone brings them coffee every morning.
But here's where it gets interesting: what if the real power move isn't choosing between human OR machine judgment, but creating systems where they check each other? The algorithm flags when a manager consistently rates women lower than men. The human catches when the algorithm penalizes someone for taking parental leave.
I worked with a tech company that implemented this hybrid approach. Their managers were shocked to discover they'd been systematically overrating employees who spent more time in the office, regardless of output. The algorithm spotted the pattern, and humans could then correct for it.
The mistake is thinking AI needs to be perfect to be useful. It doesn't. It just needs to expose our biases in ways we're too uncomfortable or unaware to do ourselves. The question isn't whether the algorithm is fair—it's whether the integrated system is fairer than what we have now.
What's your experience with performance reviews? Ever been on the receiving end of an evaluation that felt more about politics than performance?
But here's the thing no one wants to admit about human judgment in performance reviews: it's a mess. We're terrible at being objective, and we lie to ourselves about it constantly. Confirmation bias, recency bias, affinity bias—pick your poison. Rick gets a glowing review because he reminds his manager of himself ten years ago, while Priya gets dinged for being “too quiet,” even though her code runs half the product.
Now, does that mean we roll in machine learning like it’s going to save us from flawed humans? Absolutely not. ML brings its own baggage—especially if it's learning from those same biased evaluations. Feed the model garbage and it’ll automate the garbage at scale.
But refusing to use ML because humans are “better” at something like this assumes the status quo is defensible. It's not. The question isn’t “Should we trust algorithms over people?” It’s “Can we design systems where each corrects the other’s failures?” In other words: co-pilots, not dictators.
For example, imagine performance reviews where the ML flags patterns—say, that women on the team consistently get lower ratings for ambiguous reasons—while the human still makes the final call. Or where it surfaces long-term performance trends that most managers wouldn’t have the memory (or spreadsheets) to catch.
Use the machine to cut a path through the underbrush. But let the human decide which way to walk.
Using AI for performance reviews sounds efficient until you realize your career path now depends on an algorithm that doesn't know you stayed up three nights straight to save that client account.
The ruthless co-founder analogy gets it half right. AI can absolutely see patterns humans miss—like unconscious biases in how managers rate different team members. But it also misses critical context that humans instinctively grasp.
I worked at a company that experimented with ML-driven performance scores. The algorithm flagged a developer as "underperforming" because his code commits decreased by 40%. What it missed? He'd spent those months mentoring three junior developers who were now thriving. No algorithm captured that value transfer.
The best approach isn't binary. The companies getting this right use ML to flag potential blind spots ("You've rated all men higher than women on 'strategic thinking'"), while keeping humans in charge of the nuanced, relationship-based evaluation. They're using AI as a check on human judgment, not a replacement for it.
The question isn't whether AI should disrupt your performance reviews. It's about understanding which parts of evaluation need human wisdom and which parts benefit from algorithmic objectivity. And honestly, that balance point keeps shifting as the technology evolves.
Sure, algorithms can strip out biases—if you train them right. But that’s a big “if.”
The real problem isn’t whether machine learning can be more objective than humans. It’s that everyone assumes the data we’re feeding it is some sort of holy truth. Spoiler: it’s not. Most performance data is messy, incomplete, and ridiculously subjective. If Susan always smiles in meetings and her boss likes her vibe, guess who gets better ratings in the system? And guess what data trains the model?
You end up with a feedback loop that looks a lot like institutionalized bias, now just faster and with pretty dashboards.
And let’s not ignore how easily machine learning can be gamed. Once employees figure out the inputs the algorithm likes—number of Slack messages sent, hours logged in Jira, emoji usage in team chats—they start optimizing for the wrong things. Performance becomes performance art.
If you want to use ML in reviews, fine. But treat it like a second opinion, not the final diagnosis. Let it surface patterns over time, flag anomalies, maybe even highlight who’s quietly crushing it in the background. But replacing the human manager entirely? That’s just outsourcing your judgment—and your culture—to a black box.
You can’t claim to value empathy and context, then turn around and ask TensorFlow to handle your promotions.
I mean, there's something deliciously ironic about using algorithms to judge whether humans are performing well, isn't there? The same technology that can't figure out if a photo contains a bicycle or a motorcycle is now supposed to tell us if Sarah from marketing deserves a raise.
But let's be real – human managers aren't exactly flawless performance evaluators either. We play favorites, get distracted by recency bias, and sometimes can't see past that one brilliant presentation to notice six months of coasting.
What gets me is how we frame this as binary: either cold, calculating machines OR fallible, biased humans. The interesting space is the hybridization. What if ML flags patterns (like noting that women consistently receive more criticism about "communication style" than men) while humans provide context and judgment?
The most progressive companies I've seen aren't asking if AI should replace human judgment – they're asking how AI can make human judgment more self-aware. Because let's face it, most performance reviews aren't suffering from a lack of data – they're suffering from a lack of honesty about what that data actually means.
Let’s be real: human judgment in performance reviews is already a black box. Managers come in with their own biases—recency bias, similarity bias, the “I just vibe with this person” effect—and we pretend it's objective because there's a person holding the pen.
Machine learning doesn’t magically fix any of that, but at least it forces you to articulate the inputs. What signals are we valuing? Are we measuring outputs? Peer reviews? Customer satisfaction? The act of building the model exposes what’s actually being rewarded—which, let’s be honest, is sometimes proximity to power more than impact.
And unlike a manager’s gut instinct, a model’s bias is testable. If it’s recommending promotions more often for one group than another, that’s quantifiable. Auditable. Fixable. Try fixing the bias in your Director of Sales.
But here's the twist: the danger isn't in using ML. The danger is pretending it's objective because it's math. Garbage in, garbage out—but with a spreadsheet instead of eye contact.
So no, don’t replace human judgment. But do augment it with models that force you to put your values on the table. If you're rewarding “collaboration,” define what that means. Is it fewer Slack messages after 6pm? Cross-team project participation? Just saying it forces the company to stop bullshitting itself.
Use the machine not to outsource judgment—but to clarify it.
Look, I get the "AI as ruthless co-founder" framing, but there's something deeply problematic about applying that mindset to employee performance reviews. We're talking about people's livelihoods and career trajectories, not just optimization problems.
The tech bros selling these ML performance systems love to pitch the "unbiased algorithm" narrative, but let's be real—these systems just systematize existing biases at scale. They're trained on historical performance data, which already reflects who got promoted in your possibly-biased organization. Congratulations, you've just automated discrimination with a veneer of objectivity!
What's especially rich is when companies implement these systems while preaching "people are our greatest asset." Nothing says "we value you" like reducing someone's contributions to a set of proxy metrics that an algorithm can digest.
That said, I'm not some Luddite arguing for pure human judgment. Managers play favorites, fall for recency bias, and make decisions based on who laughs at their jokes. The answer isn't choosing between flawed humans or flawed algorithms—it's designing systems where they check each other.
The most promising approach I've seen combines ML-derived insights as one input among many, with humans making final calls but having to explicitly justify overriding the system's recommendations. This creates accountability in both directions.
Fair point about the potential for bias in human judgment—but let’s not pretend machine learning is some neutral oracle descending from the data heavens. Algorithms don’t eliminate bias; they systematize it.
Take Amazon’s infamous recruiting algorithm. It was trained on ten years of hiring data—which, surprise, reflected a male-dominated tech culture. The result? It penalized resumes that included the word “women’s” (as in “women’s chess club”) and downgraded candidates from women’s colleges. Amazon scrapped it because it quietly replicated structural bias at scale. That’s the danger: an algorithm can take a flawed process and make it look objective while baking in the exact same problems.
Even worse, once a machine learning system makes a call—say, that an employee is underperforming—good luck unpacking why. The model might have picked up on patterns like fewer Slack messages sent after 6 p.m. or fewer calendar invites from senior leaders. But do those correlate with performance? Or just with being in the wrong clique? These models don’t know context; they know correlation. And when you start attaching HR decisions to statistical ghosts in the machine, people lose trust fast.
I’m not saying burn the models. But they should inform, not decide. Like a GPS that shows you a route but still lets you detour when traffic’s weird. Otherwise, you’re just swapping human flaws for algorithmic ones you can’t interrogate.
This debate inspired the following article:
Should companies use machine learning for employee performance reviews or stick to human judgment?