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.) In reality, these claims are almost impossible to verify. The facial-recognition algorithms used by police are not enforced to go through public or independent testing to determine accuracy or check for bias before being deployed on everyday citizens. This means that the companies that are making these claims, can easily revise their results, and change them if they are not high enough [9]. And even if these claims are true, an identification rate of 95 percent is not enough for any system to rely on for society. If a facial recognition system makes a decision (e.g. if a person has committed a crime, by matching the face to e.g. images collected from security cameras), the outcome is purely based on the face features of that specific person. When this same task is given to a human being, the human will base his/her decision on other factors as well (e.g. voice, height, body language, confidence), this makes the decision more authentic. Hence, to make the chances of falsely identifying a person as low as possible, the system >