You remember a time when you are so passionate about your job. Going to the office and applying yourself every day was such a pleasure. But now, the work environment is no longer pleasant.
The job has become tedious and repetitive. There is no challenge in the position, and you start to feel like you are stagnating.
You have been thinking of handing in your resignation. One thing stops you, though; lack of a Plan B. So now, instead of jumping out of bed, you have to drag yourself out of it.
You look for every opportunity to be away from the office. However, it is getting hard to put on a poker face every day, hoping no one discovers how tired you are.
But wait, do you know that every time you scan your face to gain entry into the building, AI reads your moods? And that’s not all. The tattletale technology is giving management reports every single day.
Yes, they are getting insights into your ability to perform based on your emotions. It sounds like a scary movie, right?
Well, read on as we explore the pros and cons of AI face recognition to track employee moods and performance.
Facial Recognition Technology; What Is It?
Facial recognition technology uses biometrics to identify similarities in two facial images. The software uses information from a database of photos or videos to identify a person. In its basic element, facial recognition is a security device.
You can’t access the building if the algorithms do not authenticate you. The same applies to locking devices like smartphones.
But now, the technology is evolving further with some unique features. For example, facial emotion recognition identifies things like disgust, anger, or fear. And companies are adopting the use of such in their organizations.
Pros of Facial Recognition Technologies
Better Recruiting Processes
Facial recognition technologies can help in the recruiting process. AI recruitment platforms have become popular because they remove bias from the hiring process.
HR uses them to source candidates based on experience and best fit for the job. And, emotion recognition software ‘read’ candidates without relying on what they say. Instead, the algorithms determine candidate attitudes, confidence, or feelings. It allows for a better assessment of new hires.
The AI face analysis scans word choice, facial expressions, vocal tone, and movements. It generates data-driven insights to help in decision-making. This removes subjective judgments or biases that the interviewers may have.
Improving the Work Environment
Companies can identify employee morale using AI face recognition technologies. It is especially helpful where employees may be afraid to express themselves.
HR can get insights with AI facial emotion recognition software to get insights. By looking at facial features, AI systems can determine some things. Such include stress levels, emotional responses, and happiness.
Cons of AI Facial Recognition
Ethical and Privacy Considerations
One of the biggest issues around facial recognition technologies is privacy. Constant surveillance can increase stress levels amongst employees.
What happens if you know that someone is watching your every move, including nonverbal cues? Well, for most, it will hurt performance.
The only thing it will do is make employees look for ways to con the system. They will self-censor or change behavior if they know they are under surveillance.
Facial Expression and Emotions
Some concerns exist about the science behind such technologies. There is no scientific proof that facial expressions reflect an emotional state.
Take the example of a customer care agent or air hostess. Their job requires them to smile at all times. But, this does not always represent what they feel inside.
Such technologies base results on basic emotions. These include fear, anger, sadness, surprise, disgust, and joy. But, human emotions are complex.
An employee getting a promotion could experience joy and fear at the same time—joy for the new opportunity and fear at the expectations and his ability to handle them. But, unfortunately, AI technologies cannot generate such insights from facial expressions.
Lack of Diversity in Data Sets
AI technologies rely on data to generate reliable results. Insufficient or lack of diversity in the datasets can generate inaccurate feedback.
There is still a lot of validation that needs to go into ensuring accuracy. It would be hard to understand an employee’s behavior using blanket baseline data. For example, a study by MIT showed a 35% error margin for dark-skinned women.
There may even be bias towards black men who appear angrier than white men. So it brings in the element of bias, which technology should remove.
Calls for Regulation of Such Technologies
The adoption of AI emotion recognition technology is subject to use. This is because there is no rigid industry regulation on implementation. However, some organizations provide some type of guidelines, including GDPR.
But, private use is largely unregulated. Companies that collect such biometric data must ensure its security.
AI facial recognition technologies have helped in the field of security. But that’s not all, companies can assess employees’ moods as well. A happy employee is likely to be productive.
Those who are unhappy or have low morale will have challenges performing. Identifying such employees allows the company to take proactive steps. High employee turnover can be expensive for the business.
Retaining the existing staff is the best option at all times. Emotion recognition software provides insights to help improve the work environment.
But, like any other technology, there are some concerns. Concerns around employee privacy and ethical implementation. Management of the biometric data is also key to ensure the safety of the information.
The science behind the technologies also raises some concerns. Using basic human emotions does present its challenges.
The software cannot capture the complex range that characterizes humans. Organizations must pay due consideration to such when using the technologies. It may be a long time before we replace human judgment and rely solely on AI.
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