Although we do not notice it, automatic learning is an element present in our lives.
Different algorithms learn on their own and choose the ads we see, identify us when we cross borders, let our cars know when they have to brake, etc.
Among all these cases, facial recognition is perhaps the most powerful.
Current technology allows us to recognize tens of thousands of points of our face in tenths of a second.
Sophisticated algorithms know a lot about from a simple photograph.
From the arrival of facial recognition to smartphones, popularized by Apple in 2017 and expanded by other companies, our phones have begun to understand our facial features to know if we are us or not.
According to technology makers, our faces represent us much better than fingerprints.
They state that this biometric method has greater precision in the way in which they are implemented.
There are fewer false positives and they also learn how our faces vary throughout the day: with glasses, with a beard, with a dirty face, clean, etc.
Face recognition is trained with huge amounts of photographs and videos of different people.
Although they can be strong operating guides, they begin to understand on their own, hence ‘automatic learning’, by analyzing, again and again, the data presented.
If the faces of the learners are mainly of a specific ethnicity, the system can specialize in its features, and render it unusable for others.
The same facial recognition system that is able to recognize males with a high level of accuracy can also be programmed to recognize females, minors and the elderly.
The same algorithms that detect diseases can alert of possible pathologies that we do not want people to know.
A camera at work can alert our bosses to our health conditions, or insurers can use face recognition to reveal personal medical data.
This can affect our privacy by taking part of our medical data to anyone who can analyze our face: social networks, companies of all types, gyms, etc.