Diversity in the workplace

Increase diversity and equality in your hiring process. Our game-based assessments are Bias-Free by design. Trust facts, not-gut-feelings.

We are all biased. That’s how nature works. Our brain is constantly evaluating the world around us and associating it with already existing knowledge and previous experiences. We unconsciously avoid a street where a dog scared us last week. When we eat a nice french fry in Antwerp, a bag of fries seems like a good idea to us in Ghent the year after. This associating happens on both a conscious and subconscious (unconscious) level. Because of this we have conscious and unconscious biases. We all participate in it, regardless of background, education or gender. Prejudice has a great advantage: if we had to think deeply and consciously about all decisions, it would be too burdensome.

Unfortunately, this form of “being programmed” also has a large disadvantage namely; unconscious bias. Because we continuously have to filter and cluster information, we automatically, and therefore unconsciously, put things in a ‘box’. Very natural, but sometimes with major consequences that are not desirable.

Bias and (lack of) diversity in recruitment

Bias in the selection process is a big problem. The shared recruiters’ motto ‘Every talent a fair chance’ can be compromised by (un)conscious biases. As soon as candidate information, such as CV, is read, the brain starts using it and draws all kinds of (premature) conclusions, based on the tone of voice, name or even the photo for example. Stereotyping is still a common thing, and so there are numerous other cognitive biases to be distinguished.

Preventing discrimination in the selection process is receiving more and more attention, also at the legislative level. This should ensure that employers do not consciously or unconsciously discriminate. But when are you really bias-free? And is this even possible?

Say bye-bye to biases

Would you like to put an end to bias in your recruitment process in the short term? Read up on it and don’t let them sell you a quick fix. These 3 urgent advices will help you on your way:

1. Measure potential instead of current performance

Minimizing bias in the recruitment process starts with the most important thing: objectively mapping out the desired behavior and required skills of the future employee. Then you use a reliable & objective assessment to determine whether the candidate has the qualities that are essential for the job. You do this even before you look at the life cycle. So first (pre-)select on performance potential instead of on the basis of experience, education and/or current skills.

A regular CV screening cannot be done without bias. By using an objective measurement of potential you reduce bias to a minimum. With the brain-based NeurOlympics games you measure the building blocks of performance potential: objective, non-trainable and reliable.

2. Use an assessment that is actually bias-free

But: be careful. There are plenty of assessments on the market that claim to map qualities ‘bias-free’. To be able to make a good assessment of whether an assessment is actually bias-free, it is important to know exactly what it means. Bias-free is defined in recruitment as: the exclusion of prejudice in the selection process. For example, bias based on ethnicity, gender or level of education. This is easier said than done. Blind assessment alone is not enough. Self-report instruments, for example, can carry large degrees of bias in personality measures such as extraversion that are highly culture-specific. In addition, candidates give socially desirable answers (also a form of bias) rather than expressing their own opinions. The use of complicated words in questionnaires can also disadvantage candidates that are not native speakers of the language the questionnaire has been written for.

To measure as bias-free as possible, you need to look for an assessment that makes no, or less, use of things that can be influenced by for example, access to schooling, previous education or language proficiency. Examples of those measures are: brain activity, blood values and cognition. Even then, there are differences that you need to correct for. Like age, gender and level of education. You need to know this personal data to be able to actively correct for bias. This correction is of even greater importance if you want to start predicting bias-free work performance. BrainsFirst has taken care of all this in the core architecture of the NeurOlympics assessment platform and the data analyses.

3 – Be keen on AI.

Algorithms can take the human (un)conscious noise out of the selection process. You have to be alert here that AI models are actually always a caricature of the data you put into them. If 80% of your department consists of field hockey playing TU-Delft men, then your algorithm will prefer, precisely, field hockey playing TU-Delft men. So you will have to try to avoid bias in what you are trying to predict. And once you see that there is indeed bias in what you are trying to predict, then you need to take active measures against it. At BrainsFirst, this is basic principle 3.

Added together, these 3 elements lead to significantly higher (cognitive) diversity.

Start today with diverse and inclusive selection

A diverse and inclusive workforce with future potential. This is exactly what BrainsFirst helps organizations with.

Together with our experts, you as a client determine what skills and professional behaviors you are looking for in the employee you are recruiting for. With this input a brain-based target profile is created. This is a match model based on cognitive skills. Candidates, internal or external, are invited to play the NeurOlympics which measures cognitive functions. Each candidate’s data is then individually corrected for bias. By this, each candidate is carefully, honestly and unbiasedly matched to the pre-designed target profile.

In this way, we help organizations to take an important step in counteracting bias while getting the right persons in the right places.

Start now with hiring bias-free

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