How Emotion AI Can Transform Large-Scale Recruitment ProcessesHow Emotion AI Can Transform Large-Scale Recruitment Processes
How Emotion AI Can Transform Large-Scale Recruitment Processes
November 7, 2018
by George Pliev
If you are reading this, then you have probably heard that AI, combined with robotics, is set to transform recruitment and talent acquisition. Sadly, nobody has explained exactly how this will occur.
Video analytics, combined with emotion AI, is how.
For the last few years, platforms for video interviews have provided a helping hand in large-scale recruitment efforts. A 5-minute long video can save 60 minutes of a recruiter’s time, helping to avoid interviews with the wrong candidate.
Based on insights from corporate recruiters, a company with 20,000 employees has on average 500 vacancies, receiving 10 applications for each. This means dealing with hours of video interviews. Unlike written resumes, live videos cannot be shaped to be easier to watch.
Automated systems for emotion analytics are able to detect behavioural patterns that people express through their body language, voice, and expressions.
Not only this, but they will also soon be able to recognize more subtle cues that provide data about cognitive states, such as mental load or engagement, and social attitudes, like hostility. Work ethic, confrontational tendencies, negotiability, and attentiveness are important factors for hiring decisions. While these are not detectable through the usual tests, they can be extracted from behavioral patterns.
Here are the three ways in which Emotion AI helps the decision-making process.
By watching a 5-minute long video of a candidate answering some initial questions, a recruiter is able to form a general idea of that person.
In fact, many argue that the decision to hire a candidate is made within the first few seconds of a video, similar to how it’s arguably made within the first 6 seconds spent reviewing a resume.
Behavioural statistics can help confirm the recruiter’s intuitions and avoid biased decisions. This is crucial, as human bias in large-scale recruitment is practically unavoidable due to the sheer volume of applications.
On the basis of the recruiter’s personal markup and previous successful hires, Emotion AI can offer recommendations for new candidates. It’s no secret that companies would prefer their candidate to smile, to not show aggression, and to be self-secure.
Given the potential for profit making that a successful hire can bring to the company, it’s normal for certain tendencies that are associated with a certain type of activity to be desirable. These traits can, helpfully, be associated with a particular behavioral and emotional profile.
In large-scale recruitment, where there is great probability of an error, the role a human recruiter assumes in the process should be minimal. Emotion AI can provide important pre-decisions on a potential hire by analyzing the statistics and taking into account previous recommendations. Behavioural profiles can be a huge support to recruiters looking for a certain skillset.
We see that innovation is penetrating more slowly in human resources than in any other field. But the time has come to welcome innovation in HR. Emotion AI brings positive changes on many levels: avoiding bias with behavioral statistics, minimizing wrong hiring decisions with the help of recommendations, and finally solving lengthy task repetition while providing pre-decisions. This is a win-win situation for both the company and the candidate.
George is a Founder and CEO at Neurodata Lab. He is passionate about innovations and has vast experience working with start-ups and managing several venture funds. George has a BS in Law and Legal Studies.
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