Beware of the Hidden Biases Around Employee Recognition

When was the last time you reviewed your recognition and reward program data to see if there is any tendency toward hidden biases?

A hidden—or implicit—bias is defined as a preference for, or against, a person, thing, or group, which is held at an unconscious level. This means you and I don’t even know our minds are holding onto this bias. In contrast, an overt—or explicit—bias is an attitude or prejudice which is very much endorsed at a conscious level.

For example, what is the proportion of recognition or reward recipients who are male versus female, with respect to your employee gender ratio? Are rewards given more often to one gender over another? Is there any general ratio between white and non-white employees? Do disabled staff equally merit and receive recognition and rewards for exemplary work?

Perhaps we all need to ask these kinds of question when identifying whether hidden biases exist in our recognition and reward practices and programs.

If there are certain principles that keep recognition and rewards open it is fairness and equity.

How well is your organization doing in this area?

Reality Check of Hidden Biases

Harvard University conducted a Project of Implicit Bias Tests and found from their research that 70 percent (possibly even higher) of hidden biases, were directed toward African Americans, the elderly, the disabled, and overweight individuals. 

Harvard’s Implicit Association Tests examine attitudes and preferences around sexuality, weight, countries, age, skin-tone, gender, and race of people. You can even go here to take these tests for yourself and learn how you do.

In your role as managing recognition, you could create a Bias Dashboard as a review process for recognition and rewards and display examples of recognition and reward discrimination as reported, or observed, correlated against analysis of your recognition and reward program data. This requires identifying employees by the categories listed above. You would then conduct an analysis of your recognition and reward program usage data against these criteria.

Learning to Eliminate Bias

Let the objectivity of your recognition and reward data inform and direct your decision making and ongoing learning and development around diversity and inclusion, and the need to eliminate unwanted biases. 

Set goals to ensure there is diversity awareness training around giving unbiased recognition and rewards. Never go from the mindset of setting quotas for different people receiving a certain number of recognition and reward experiences. Rather, strive to overcome any unconscious bias that could alienate certain people from being properly recognized and rewarded.

Each of us has a responsibility to call out unfair recognition and reward practices. By looking at each person as someone of worth and potential we will better see how to express our gratitude and appreciation for them and recognize their contributions.

It is unfortunate that our minds may not be as inclusive as we think they are. The human brain is hard-wired to make quick decisions based on black and white facts and figures gleaned from our life experience and knowledge learned. When it comes to people, you and I tend to generalize and immediately create “them” and “us” scenarios, which biases us in how we behave and act towards others.

Giving recognition the right way and using rewards objectively and fairly will go a long way to making everyone feel inclusively valued as people and for what they do.

Recognition Reflection: How is your organization working on eliminating hidden bias, so people give recognition fairly?

Roy is no longer writing new content for this site (he has retired!), but you can subscribe to Engage2Excel’s blog as Engage2Excel will be taking Roy’s place writing about similar topics on employee recognition and retention, leadership and strategy.

Please note: I reserve the right to delete comments that are offensive or off-topic.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.