Removing Bias from Employee Evaluations

March 16, 2023

Humans make thousands of decisions every day. To handle that volume, our brains need ways to save time and energy. We need shortcuts. And these decision-making shortcuts are commonly referred to as “biases.”

While cognitive biases intrinsically are neither good nor bad, certain biases can have a detrimental effect on our decision-making processes. Four of the most common types of biases include:

• Similarity Bias – We instinctively sort people into “like me” and “not like me” categories. We tend to view those who are similar to us positively and are skeptical of those who are different. Consequently, managers often hire people who remind them of themselves.
• Experience Bias – We take our perception to be the objective truth. Experience biases occur when we assume our view of a situation constitutes the whole truth and fail to remember that others may see the world differently.
• Distance Bias – We prefer what’s closer over what’s farther away and have the instinct to prioritize what’s nearby.
• Confirmation Bias We tend to process information by looking for results consistent with our beliefs. We want to be proven correct. Consequently, we look for ways to interpret data to confirm our thoughts.

Having identified four common bias categories, let’s explore four strategies to identify and eliminate any bias in employee performance reviews.

Meet With Employees More Frequently

In our new normal of remote work, distance biases have become all too common. In a meeting with people on-site and people joining via a video platform, it’s common for leadership to dismiss those who aren’t physically in the room or give less importance to their work.

To avoid distance bias, management can meet with employees more frequently to increase familiarity and make people seem closer. During company meetings, leadership must periodically greet remote workers first and consciously acknowledge remote colleagues before discussing matters with the room.

Develop a Clear Evaluation Structure

Management is often prone to similarity biases when considering who to hire, who to assign to special projects, and who to pay more. To overcome a similarity bias, leadership must seek common ground with people who appear dissimilar.

Leadership can remove bias from decision-making by developing a transparent evaluation structure and communicating clear performance metrics to their teams. Either you can hit the fastball, or you can’t.

Gather Feedback from Multiple Sources

Performance reviews and evaluations often fall victim to the experience bias of the person administering the assessment. It’s common for managers to assume their view of a situation constitutes the whole truth. To eliminate experience bias, we can check our thinking by including other perspectives on how a worker performs.

Instead of holding semi-annual performance reviews, create an office culture of continuous coaching where leadership regularly meets with workers to make minor course corrections and form informed consensus.

Look at Performance Metrics

Charts and graphs show statistics and information. The data is neutral. However, the interpretation of the data can be far from neutral. Thankfully, we can mitigate these confirmation biases by looking at our beliefs, searching for ways to be proven wrong, and carefully listening to all sides before deciding.

While humans are hardwired to be biased in decision-making, HR leaders can reduce and hopefully eliminate bias from employee evaluations by being mindful of biases. Meeting with employees more often, gathering multiple opinions to build consensus, and establishing a transparent evaluation system with measurable indicators will significantly strengthen your organization’s commitment to diversity, equity, and inclusion.

The views and opinions expressed within are those of the author(s) and do not necessarily reflect the official policy or position of Parker, Smith & Feek. While every effort has been taken in compiling this information to ensure that its contents are totally accurate, neither the publisher nor the author can accept liability for any inaccuracies or changed circumstances of any information herein or for the consequences of any reliance placed upon it.