{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","uuid":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","normalized_title":"ifthenalgorithmicpowerandpolitics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","isbns":["2017054909","9780190493042","9780190493059"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":211,"navigation_url":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053","title_url":"https://workshop.closeclaw.top/ai/title/If...Then%20Algorithmic%20Power%20and%20Politics","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/1d3bd28e-c1ba-4af2-8580-60b32ef26053","page_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page","search_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/search"},"page":{"printed_page":26,"pdf_page":36,"physical_index":36,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"44a9558a8352c88ebcbbb428cf17c138481bf0ae8f203451e3a3d7470f4fc685"},"chapter":"IF...THEN","content":"The botom line is that there are many diferent algorithms that can be used to “impose a shape on the data,” as Mackenzie put it. For predictive modelling, for example, well-known algorithmic techniques include: logistic regression models, the Naive Bayes classifier, k-nearest neighbors, support vector machines, random forests and neural networks (Mackenzie,  2015). However, as Bernhard Rieder (2017) points out, something like the Naive Bayes classifier is not yet an algorithm in a more restrictive understanding of the term. Rather, it outlines a common method—an “algorithmic technique”—used to solve specific computational problems. If we want to make it into an algorithm that can be run, we have to further specify it. For random forests, for example, you would have to specify the number of trees, and the number of splits to try in each tree. For neural networks you would have to specify the depth of the hidden layers. So in some sense, we are far away from an algorithm. Yet there are also algorithmic techniques for specifying these so-called hyperparameters.13 Many pieces of software also come with default parameters. Once you specify these parameters, and you have the data, then it is an “algorithm” in a narrow computational sense, whereas without them it is incomplete. What determines whether to use one technique over another “depends upon the domain (i.e., loan default prediction vs. image recognition), its demonstrated accuracy in classification, and available computational resources, among other concerns” (Burrell, 2016: 5). Take, for example, the problem of face recognition. With more than 350 million photos uploaded onto Facebook every day, face recognition is an important area in which Facebook is using machine learning.14 Algorithms are trained on a variety of existing data sets to learn how to detect faces in the wild. Depending on the specific task of recognition, however, diferent algorithms can be deployed. One of the simplest and fastest learning algorithms for this purpose is the nearest neighbor (Domingos, 2015: 179). In order to determine whether an image contains a face, the nearest neighbor algorithm works by finding the image most similar to it in Facebook’s entire database of labeled photos. “If” it contains a face, “then” the other one is presumed to contain a face as well. This approach is used, for example, in Facebook’s auto-tagging functionality. Given Facebook’s vast data pool, the company’s engineers are able to train algorithms to detect faces in ways that most photo-sharing sites cannot. Facebook’s artificial intelligence team has recently developed a system called DeepFace, which is supposedly able to identify faces at a 97.25% accuracy level, which is just slightly worse than the average human score of 97.53% (Taigman et al.,  2014). This system uses what is called “deep learning,” a technique that currently constitutes one of the leading and most advanced machine learning frameworks available. Deep learning is based on neural networks, a model most widely used in image and speech recognition. Modeled to emulate the way in which the human brain works, neural networks “use diferent layers of mathematical processing to make ever more sense of the information they are fed” (Condlife, 2015).15 In terms of image recognition, a system powered by neural networks, for example, would analyze pixel brightness on one layer, shapes and edges through another layer, actual content and image features on a third layer, and so on. Using diferent algorithms on every layer to process the information, the system would gain a more fine-grained understanding of the image.16\n\n26\n\nIF . . . THEN","previous_page":25,"next_page":27}