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How Emotion Recognition Technology Helps Recruiters - By : Marie-Anne Valiquette,

How Emotion Recognition Technology Helps Recruiters


Marie-Anne Valiquette
Marie-Anne Valiquette Author profile
Marie-Anne Valiquette obtained a Bachelor's degree in Mechanical Engineering at the École de technologie supérieure (ÉTS) in Montreal. She lives in Silicon Valley, California where she studies artificial intelligence through online platforms like Udacity and deeplearning.ai.

Artificial intelligence identifying emotions by face expression

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Facial recognition technology is allowing us to purchase meals, access our smartphones or computers, and is even used by law enforcement to locate and arrest criminals. This technology even goes further as algorithms can now recognize not only our identities but also how we feel. Emotion recognition technology is still in its early stages, but some artificial intelligence (AI) developers are claiming it can be used to transform recruitment.

These algorithms can analyze how nervous, interested, honest or untrustworthy an applicant can be, making it easier for employers to discard an applicant early in the hiring process. Major companies like Unilever are already starting to incorporate this technology into their recruitment process. Human, a startup located in the UK, China and the US is developing cutting-edge technology to capture and analyze human feelings and character for various uses [1]. As an example, they use facial analysis algorithms in video format to observe job applicants. They can decipher the emotional responses of candidates to the content of the interview. Human will then send back to the recruitment department their analyses for each interview question thus providing greater insight to foresee the best candidate and enabling better decision-making. Emotion recognition technology has the potential to make it much easier to choose the most promising candidate.

Affectiva – Example of real-time emotional and cognitive state

It is not common practice: companies are still screening applicants through conventional methods. However, this requires many hours, which is not particularly efficient when choosing the best candidate for a position. Also, a recruiter is likely to use certain biases in an interview scenario based on attraction, ethnicity, or gender [2]. Emotion recognition technology provides a more objective way to analyze the suitability of a candidate.

iMotions Facial Expression Analysis Solution

There are several problems regarding the use of emotion recognition technology in recruitment. One is the legal custody of any recorded data, and whether applicants will retain their own image rights or whether they can sign it off to prospective employers [2]. Another is how reliable and accurate the analysis is: human beings are complex and, depending on culture and context, an individual’s expression or reaction towards a question may not be the most important indicator as to decide if the candidate is competent. Also, eccentrics, shy people, and introverts could find themselves factored out by an algorithm; that doesn’t mean that they are not qualified for the position. Not behaving in a way that aligns with a new, desirable norm is not a sign of incompetency.

Any data collected by emotion recognition technology would also require training to gather, interpret and extract the most useful and accurate information during the interview session. Another question that needs to be discussed is whether candidates will change their behavior knowing they are being recorded.

The areas in which this technology has the potential for business does not end with the interview process. It could also be deployed to help current employees to improve their presentation techniques or to scan employees for signs of boredom, depression or fatigue. At the other end of the spectrum, this technology can be used to penalize employees if unproductive, with the adverse effect of reducing privacy in the workplace.

Marie-Anne Valiquette

Author's profile

Marie-Anne Valiquette obtained a Bachelor's degree in Mechanical Engineering at the École de technologie supérieure (ÉTS) in Montreal. She lives in Silicon Valley, California where she studies artificial intelligence through online platforms like Udacity and deeplearning.ai.

Program : Mechanical Engineering 

Author profile

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