s 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 Artificial Intelligence learns from the examples it was trained on [19][22]. In conclusion, the performance of face recognition algorithms suffers from a racial or ethnic bias. The demographic origin of the algorithm, and the demographic structure of the test population has a big influence on the accuracy of the results of the algorithm. This bias is particularly unsettling in the context of the vast racial disparities that already exist in the arrest rates [22][10]. iii. System still needs a human judge The last problem that will be discussed in this paper is that the technologies that are existing today are far from perfect. Right now, companies are advertising their technologies as “a highly efficient and accurate tool with an identification rate above 95 percent.” (said by Facefirst.>