“…chances are high that recruiting
and retaining talent end up in the top three challenges
an organizations face.” – Sachin Gupta, HackerEarth CEO and co-founder
Consciously or unconsciously there are hiring bias attached to every decision we make.
Making the right decision when hiring is essential to any company as it will determine if the new hire will be a perfect match for the required skills, the team attitude, and the company culture.
Today, technology and data have been of immense help while making recruitment decisions.
We have more information, and we are able to combine this information to make the best possible decision for any occasion.
Nevertheless, the ones who will make the decision, in the end, are people, and as humans, our decisions sometimes are not only based on facts and logic but on emotions and personal experiences as well.
We are vulnerable to bias.
As an HBR researcher said, “bias causes us to make decisions in favor of one person or group to the detriment of others.”
Recruiters often make decisions based on the age, race, ethnicity, beliefs, and gender of their candidates.
Such decisions have a direct effect on the quality of the person who will be hired at the end of the process.
The reasoning is that when selecting people based on the aforementioned aspects, we miss the opportunity to consider candidates who may better match the required knowledge and the necessary skills and attitude for the position we are hiring for.
- Look for knowledge and talent
- Match the necessary skills
- Select for attitude
- Check the gender of your candidates
- Focus on age, race, and ethnicity
- Look for similar background
7 types of hiring bias
This type of bias applies when people create a hypothesis in their mind and look for ways to prove it.
The best example showing how we use confirmation bias is when we go through a CV and see that the candidate attended Harvard Business School, we expect the candidate to be a top performer because he or she is from an ivy league school.
We keep our thoughts away from considering other possibilities such as that this particular candidate could be an exception or that despite the skills he/she may possess, the candidate may be a mismatch for our company culture.
We apply affinity bias when we look for people who went to the same college as we did, or who grew up in the same city as we did.
We do not focus our attention on the necessary skills or knowledge but focus on similarities we may have with the candidate concerning our personal, educational, and professional background.
That said, finding someone from the same home city or someone who owns the same exact diploma as we do, can lead us to compromise on matching the desired skills and the full job requirements for our own similarities.
Halo and horns effect
Halo effect is the phenomenon when we assume that because people are good at doing one thing right, they will be good at doing other things right as well.
It is closely associated with the first impression. If we create a first good impression of someone, it is difficult for us to change the way we perceive this person later.
The opposite happens under the “Horns effect.”
If we formulate a negative impression about a candidate when we first meet him/her, then we tend to ignore any of his/her positive characteristics and concentrate only on unfavorable ones.
This particular type of bias occurs when a recruiter does the screening of several CVs and likes one of them more than the others.
Thus, the recruiter while interviewing all candidates will expect the candidate with the CV that he/she liked to perform better than the other candidates.
The recruiter will make decisions which are in favor of this one person whose CV he/she liked during the pre-screening process.
Consequently, all other candidates are at a disadvantage “compared” to the candidate recruiter favors.
In other words, it represents judgments we make based on our “sixth sense.” In some cases, recruiters do not have all the necessary information about a candidate, and they make assumptions based on their feelings and intellect.
Of course, sometimes you need to make decisions based on what you think, feel, or believe, but why make risky decisions when you have the opportunity to take “controlled risks”? (See the last caption of the article).
We are using beauty bias when we consider that because someone is “beautiful,” he/she will be more successful as well; or, if someone is tall, then this person has more chances to become a CEO.
This actually happens today.
Sixty percent of American CEOs are over 6 foot while only 15% of the total population is over 6 feet tall; this shows some kind of bias in terms of what we think a CEO should look like.
A funny example here is the fact that Nicolas Sarkozy-a former French President, used to wear 2-inch elevator heels to boost him to 5 ft and 8 in. and look taller than he is because all his colleagues (presidents and prime ministers of other EU countries) looked taller.
We are being effective heuristics when we judge someone’s job suitability based on superficial factors such as the shape of his/her body, the type of his/her haircut, or the tattoos this person may have.
One very interesting fact related to this type of bias is what German researchers found:
- HR professionals who participated in the study use an alarming type of bias while making decisions
- The ability of obese individuals to achieve supervisory positions was underestimated
- “Normal-weight” individuals’ ability to achieve supervisory positions was overestimated
Judging people’s ability to perform well based on their body weight/shape, their appearance, or their tattoos is quite illogical, wouldn’t you say?
Everyone is special in his or her unique way, and no one has the right to make assumptions based on the aforementioned characteristics.
The same applies to recruiters.
They should avoid making decisions based on the way someone looks, and recruiters should avoid being biased in terms of how the performance of a candidate could be related to his/her superficial characteristics.
How to avoid hiring biases
An article published in the Harvard Business Review supports that hiring bias could be reduced if we train recruiters to:
- Simplify and standardize the process – pre-formulate the questions that you will ask the candidate. Avoid asking bias-based and sensitive questions.
- Go blind for the resume review – ensure that you are focused on your candidate’s specific qualifications and talents and not surface “demographic characteristics.” (Also read: Enabling workplace diversity through blind recruitment)Give a work sample test – put them on a task that they will be doing on the job if hired. “It is the best indicator of future job performance.”
Role of talent assessment software (TAS) in reducing hiring bias
Reading and focusing on all the above types of hiring bias that could affect a recruiter’s decision during the recruitment process.
Someone could spot that most of these hiring bias types are related to the first impression that you form the moment you meet someone or are related to generic assumptions or past experiences that someone may have had.
Suggestions from the HBR article focus on two important things:
- Go blind in pre-screening
- Give a work sample test
There is a solution to tackle these two points.
At the beginning of this article, I mentioned that data and technology can be of great help in a recruiter’s job.
Today, you can avoid the stress of making the wrong decision when hiring someone.
With the help of talent assessment software, you do not have to worry anymore about how much bias you unconsciously bring in when assessing someone, making you hire someone who does not match all the desired skills and job requirements.
(Also read: 5 reasons you should use talent assessment tools)
TAS, such as the Recruit, provides us with the opportunity to evaluate a candidate’s skills prior to meeting the candidate in person or meeting his/her demographic and superficial characteristics.
It allows companies to conduct tests to screen technical (in this specific case) candidates. It automatically creates challenges, remotely evaluates candidates, and gives detailed and objective insights on their technical skills.
Recruiters can use such software to hire without prejudice during the recruitment process which could otherwise negatively affect their final decision and disappoint during performance reviews later on.
Why you need to avoid hiring bias
There are several reasons why you as a recruiter should be convinced about avoiding bias when hiring for a position. These reasons could be divided into legal-based and performance-based:
- Equal Employment Opportunity – Almost all countries require employers to provide candidates with equal employment opportunities and not be biased when hiring someone. Not respecting this law could cause a company a huge amount of money as well as fatal damage to employer branding.
- Corporate Ethics Policies – If an organization does not respect these policies, a candidate or an employee can sue the company. A lawsuit has the same consequences as described above.
- Increase Diversity – Avoiding being bias in a recruitment process allows you to hire based on knowledge and skills and not based on demographic characteristics. Therefore, your employee base, in the end, could become very diverse. Diversity, in turn, has been found to have a positive effect on an organization’s overall performance.
- Efficient Recruitment Process – When recruiters’ decisions are not biased, then the chances to hire the perfect match for the position they look to fill are higher. They do not have to be afraid of a mismatch or having to re-do the entire recruitment process.
While biased decisions are made very often, the situation is not as bleak as one expects. There are ways to avoid different types of bias and help you to make the right decision at the right time.
To achieve this, you should re-structure your recruitment process and seriously think of using talent assessment software which simplifies the process for you and provides you with objective insights into candidates’ skills.
Use HackerEarth to recruit without bias
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