Avoid prejudice in AI interviews

Sometimes, candidates with the same profile and experience are rejected in selection processes and the reasons can be found in the biases and prejudices that the recruiter may have. Talent recruitment biases are unconscious biases or biases that can influence the way human resources professionals evaluate and select candidates.. They can create impressions about a person, sometimes erroneous, that mark behaviors, often discriminatory.

La Artificial Intelligence applied to Human Resources, and more specifically to company selection processes, can help to end these biases, since it is capable of analyzing large volumes of data in an objective way, revolutionizing the way in which companies identify and select the most suitable candidates for their vacant positions.

What are the most common biases?

Affinity.

It occurs when the candidate shows beliefs, values, hobbies or background similar to that of the HR professional, and therefore the latter may initially consider him better than anyone else. For example, if the recruiter and the interviewee come from the same town and are in the big city or if both share a strange hobby.

Gender.

It occurs when one gender is preferred over another without any objective justification. For example, men are unconsciously selected for technical positions and women for administrative roles and those jobs related to the care of children and the elderly.

Age.

‍It occurs when younger or older candidates are favored without considering their real abilities and competencies. “He is too young and has no experience... or “he is too old, he will be unable to recycle”. In the case of young people, there is the paradox that youth is valued in an extreme way, given that it is common to find job offers that pray “young and dynamic environment”, while young people are included in a loop of temporary work, internships and scholarships until they are over 30, in which they are no longer considered so young. As for senior talent, for 70% of last year's hires they didn't have them.

Racial or ethnic.

It occurs when candidates are preferred or discriminated against on the basis of their race or ethnicity, regardless of their abilities and experience. This sadly happens more than is thought, for example, when a Caucasian phenotype of brown skin is presented, it reduces the chances of the employer being interested in the candidate by approximately 10%, while having a black or Asian/Amerindian phenotype doubly reduces these chances, compared to having a white Caucasian phenotype.

Aspectism.

It consists of preferring candidates based on their physical appearance, regardless of their professional competencies. In fact, being handsome or beautiful implies having a certain advantage over less graceful candidates. There was a time when “having a good presence” meant being neat and dressed properly, but this time passed. In fashion stores, restaurants, hotel receptions, gourmet stores and in general, jobs facing the public, it has been common for some time to encounter this bias, which now extends to offices and offices, among other sectors.

First impression.

‍Many studies suggest that recruiters take very little time to make a hiring decision based on the candidate's clothing. We are not only talking about dressing properly - a buttoned shirt tucked inside the pants - but about looking more expensive and branded compared to more casual clothes. In this first impression, other factors also come into play that we may not have considered but that influence the contractor's decision, such as height, confidence, how we walk or how we smell.

Confirmation bias.

‍It occurs when the interviewer has an initial perception about a candidate and seeks information to support that initial belief. This involves asking non-essential questions that confirm that belief and ignoring the signs that contradict it. For example, when interviewing the favorite candidate for the position, you may unconsciously be asked questions that help you highlight your strengths instead of asking questions that challenge you to actually share information to make a better hiring decision.

Halo effect.

It occurs when a positive attribute of the candidate blinds the recruiter and prevents him from seeing other important indicators. For example, if one of the candidates studied at a very prestigious university, it may be that without realizing his education becomes a halo for the professional, and he is so enchanted with it that he does not realize that he has had six different jobs in the last three years.

Horn effect.

Unlike the previous one, it happens when a candidate's not so attractive point makes the professional unable to recognize all the good things that he has going for him. For example, perhaps the candidate dropped out of college, and that causes the professional to overlook his six years of rapid progress in a well-known company.

Human bias is one of the biggest challenges in the selection process in companies. Artificial intelligence can help reduce this bias when making decisions based on objective data and help companies to be more diverse and inclusive in screening, identifying candidates who might have been overlooked with other types of selection processes.

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Jon Gonzalo, founder of hr bot factory, affirms that “without a doubt, with Artificial Intelligence we can achieve objectivity in selection processes, but technology will always be at the service of people and it will depend on us to eliminate these prejudices. It will be the responsibility of companies to ask their AI software to search for candidates regardless of gender, race, age and to value them for their worth and experience.”