When one is using facial recognition to prevent crime, it will also create opportunities for criminals. Stalking and identity fraud, for instance, will be made much easier. Cameras with facial recognition technology can be used to keep track of people. These cameras do not have to be expensive, which will make it more tempting for criminals to buy them, and use them for the wrong purposes. The facial recognition technologies can provide stalkers with more data about their victims: they can be used to get to know a person’s weekly schedule or to keep track of when someone is going away. It provides critical information, which for instance tells a criminal if it is safe for them to break into a house. These cameras can also be used to replace the user ID/password authentication method to access computer systems to obtain services in the name of another person. Even though the new methods can effectively distinguish the real face from fake photos by calculating the depth of the face, it is not that hard to break into a system that uses facial recognition. [3][8] US senator Al Franken has given his opinion on the problem of this topic in an open letter to the creators of an app that uses facial recognition (i.e. NameTag): “Unlike other biometric identifiers such as iris scans and fingerprints, facial recognition is designed to operate at a distance, without the knowledge or consent of the person being identified,” he wrote. “Individuals cannot reasonably prevent themselves from being identified by cameras that could be anywhere – on a lamp post, attached to an unmanned aerial vehicle or, now, integrated into the eyewear of a stranger.”. [9] ii. Racial/ethnic bias Recent research suggests that the algorithms behind facial-recognition technology may suffer from a racial or ethnic bias: many algorithms expose differences in accuracy across race, gender and other demographics [10]. It is shown in a study by P. J. Phillips [22] that algorithms developed in East Asia recognized Asian faces far more accurately than Caucasian faces. The exact opposite was true for algorithms developed in Europe and the United states. This implies that the conditions in which an algorithm is created can influence the accuracy of its results. A possible explanation for this is that the developer of an algorithm may program it to focus on facial appearances that are more easily distinguishable in some races than in others [10][22]. It is not only in the way the algorithm is programmed. It is also in the way the algorithm is trained. It is possible that a certain algorithm has more experience with Asian faces than with Caucasian faces. This unfair representation of the population which the algorithm might me used on, will lead to problems. If you do not include many images from one ethnic subgroup, it won’t perform too well on those groups because Arti>