The progressive Era (1896-1916) was a period of widespread social activism and political reform across the United States of America that spanned the 1890s and 1920s. The progressives were mostly urban dwelling, educated, middle class people involved in politics. They believed that the government needed to undergo a major change in order to solve these problems. They believed strongly in the power of science and technology in order to affect these changes. During the Gilded age, American became more prosperous and saw unprecedented growth in industry and technology. But Gilded age had a more sinister side: it was a period where greedy, corrupt industrialists, bankers and politicians enjoyed extraordinary wealth and opulence at the expense of the working class.
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.) 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 will still need a human judge.
4. Ethics
The improvement of facial recognition technologies can be a great help for national security. But it raises strong concerns regarding the individual’s privacy. When there are cameras everywhere, individuals will be continually watched. This possible violation of privacy creates fear in people. Another point that raises fear, is that the data of facial recognition can be misused. There is always tension between the need for privacy and what this loss of privacy can bring us (security). Is it acceptable to use facial recognition technologies in all situations? That is w
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