Microsoft claims its facial recognition technology just got a little less awful. Earlier this year, a study by MIT researchers found that tools from IBM, Microsoft, and Chinese company Megvii could correctly identify light-skinned men with 99-percent accuracy. But it incorrectly identified darker-skinned women as often as one-third of the time. Now imagine a computer incorrectly flagging an image at an airport or in a police database, and you can see how dangerous those errors could be. Microsoft's software performed poorly in the study.

American law enforcement agencies have created a massive facial recognition database. If you're an adult in the US, you might already be in it. According to a comprehensive report by the Center for Privacy & Technology at Georgetown Law, the law enforcement's database has 117 million American adults on file. The report says authorities used driver's license IDs from 26 states to build the database, which includes people who've never committed any kind of crime before. That's already a problem in and of itself, but it's compounded by the lack of oversight on how it's used.

A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.

Facial recognition is arguably the most talked-about technology within the artificial intelligence landscape due to its wide range of applications and biased outputs. Several countries are adopting this technology for surveillance purposes, most notably China and India. Both are among the first countries to make use of this technology on a large scale. Even the EU has pulled back from banning this technology for some years and has left it for the countries to decide. This will increase the demand for professionals who can develop solutions around facial recognition technology to simplify life and make operations efficient.

Facial recognition technology is becoming increasingly prevalent in our everyday lives, with many of us using the technology every time we use our face to unlock our smartphone – a study found that we use our phones around 52 times per day. Whilst it has transformed how we access our phones, facial recognition technology is also being used in a number of industries outside of tech to improve the service that companies provide customers with. If you're a company that isn't adopting the use of facial recognition, it's time to start researching into it before you get left behind. Devices recognise their users by scanning facial features and shapes – specific contours and individual unique features help the likes of smartphones recognise users and open certain settings up on phones. For example, many banking apps now allow users to login to their internet banking through the use of their face – this, in some ways, is far safer than the previous ways of using online banking which would either include an individual code or a series of questions to answer that only the user would know.