🚪 Performance Reviews make me want to quit HR (every year!)

TL;DR

🚪 Performance Reviews make me want to quit HR (every year!)

👁️‍🗨️ Performance Reviews: tackling bias with the SEEDS model

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Performance Reviews make me want to quit HR (every year!)

Let me just put it out there: I hate performance reviews.

Every single year, when we reach that point where managers start deciding salaries and bonuses, I want to quit. Walk out. Never do HR again. Honestly—every year!

You might ask, why such a strong reaction? Especially when most people who know me would say I’m chill as a cucumber 🥒.

Because this is when decision making biases hit their glorious peak.

5 biases that hijack performance reviews every time

1. Similarity bias

“We prefer what is like us over what is different”

Managers reward people who mirror their own style. If a manager works long hours, they’ll rate those who also stay late more favourably — even when another employee delivers stronger results in fewer hours. The “mini-me” almost always wins.

2. Expedience bias

“We prefer to act quickly rather than take time”

Speed trumps accuracy. A manager recalls the most recent or most memorable mistake and lets it overshadow months of solid work. One slip-up, and performance ratings tank. This bias works both ways, for example a single big win at the end of the year can inflate ratings (therefore reward), giving someone an easy pass for an otherwise mediocre year.

3. Experience bias

“We take our perception to be the objective truth”

Yesterday’s reputation distorts today’s reality. “Once average, always average.” Or, “They used to be great, so they must still be great.” Past impressions outweigh current evidence, whether positive or negative.

4. Distance bias

“We prefer what is closer over what’s farther away (e.g. distance, time, relationship)”

Proximity gets mistaken for performance. Employees in the same office — or those with stronger personal ties to the manager — receive higher ratings and rewards, than equally strong remote or less visible colleagues. 

5. Safety bias

“We protect against loss more than we seek out gain”

Fear of loss drives rewards. Managers overcompensate employees they worry might quit: “Let’s give them a big raise so they stay.” Meanwhile, equally valuable team members who haven’t voiced a threat to leave are undervalued.

Bias isn’t going away. We’re all human

These aren’t bad managers; these are normal humans with normal human biases. But unless we acknowledge and tackle them head-on, the cycle continues.

And when that happens, we’re not measuring performance. We’re rewarding bias.

In the article below, I’ll share how I work to mitigate these biases — not perfectly and not instantly, but one shift at a time. Because as much as performance reviews make me want to quit HR every year, I’m even more driven to reduce bias in the system and achieve more consistent decisions.

Performance Reviews: Tackling bias with the SEEDS model

In the article above, I shared the 5 biases that show up like clockwork when performance reviews roll around. You might have noticed they spell out SEEDS: Similarity, Expedience, Experience, Distance and Safety.

That’s not me being clever 😅, that’s from NeuroLeadership Institute (NLI), who coined the SEEDS model. It’s memorable and makes bias easier to spot and more importantly, easier to mitigate and challenge.

Here’s how I’ve used their recommendations when my team and I designed performance review systems and in the room when managers are deciding ratings and rewards.

1. Similarity bias → look for commonality & create a sense of us

Managers love a “mini-me.” Work the same hours? Think the same way? Instant brownie points. Instead of telling them to “ignore style,” we make sure managers set outcome-based goals well before review season. Clear goals act as the north star.

At calibration, my People Partners always ask: “Your whole team is working toward X goal. Who actually moved you closer?” Suddenly, the conversation flips from “Who looks like me?” to “Who delivered?”

2. Expedience bias → slow down and consider all information

Snap judgements are tempting, one big win or one mistake dominates. To break this, we build in evidence (objective data vs. subjective opinions). Review questions require managers to list tangible results from 360° sources (and notice I didn’t say 360° feedback? There’s a reason for that). 

In calibration, my People Partners pause and ask: “What evidence are we missing?” That single question slows the room down just enough to avoid lazy decisions.

3. Experience bias → get others to check your thinking

“Once average, always average.” Or worse, “Once a star, always a star.” Old reputations die hard. By pulling in evidence from 360° sources and running calibration, managers are forced to test their assumptions. When peers question whether someone is really “still great” or “still average,” it pulls the evaluation back into the present.

4. Distance bias → take distance out of the equation

Visibility ≠ performance. This one’s tricky, and you can’t fix it at review time; you have to bake it in earlier. One way we do this is by encouraging managers to share informal ratings quarterly with their team member (one engineering team loved it so much they made it monthly!). 

It’s not about the score, it’s about breaking the “big reveal” at year-end. Regular check-ins give employees (especially remote or less visible ones) the chance to show impact along the way. That way, quieter employees aren’t forgotten — and contributions get tracked, not just who grabs coffee with the boss.

5. Safety bias → reduce the fear factor in decisions

Managers sometimes over-reward people they fear might quit. When we see this happening during salary review, our People Partners step in with reframing questions: “If this person wasn’t at risk of leaving, would you still decide the same way?” or “If this wasn’t your team, what would you recommend?” That little reframe takes the fear factor down a notch and usually brings managers back to a fairer, more balanced decision.

Designing for bias-resistance

The goal isn’t to erase bias, it’s impossible. The goal is to design systems and use facilitation techniques that catch bias before it drives the decision.

That’s the real power of the SEEDS model: it gives us a shared language for bias and a toolkit to make performance reviews less about shortcuts and more about substance.

And yes, technically I did quit 😅 (though not because of performance reviews). But clearly, I haven’t quit caring. If you’re still stuck in those review meetings thinking, “This makes no sense!” – good news: you’re right! Better news: you’re the one who can change it!

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