{"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":27,"pdf_page":37,"physical_index":37,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ac78c09aa8ea77682a1d57186bc3ad986c63665851c774a0eef16e3282add680"},"chapter":"IF...THEN","content":"As recent cases of image recognition failures have shown, machine learning is not without its problems, and there is much to be learned from cases in which machine learning goes awry. For example, in May 2015, Google was accused of having a “racist” algorithm when its newly launched Photos app tagged two black people in a photograph as “gorillas” (Barr, 2015). A similar incident happened when the photosharing service Flickr, powered by Yahoo’s neural network, labeled a person as an “ape” due to the color of his skin. While incidents like these are perfect examples of how the results of machine learning can be very problematic, they are also good examples of how the media, which often neglect to explain why certain inferences were made, frequently reports on algorithms, thus catering to an often shallow understanding of how these systems actually work. My hope is to show how we might beter understand the power and politics of algorithms if we try to take a more holistic approach by acknowledging the complexity and multiplicity of algorithms, machine learning and big data. Incidents such as these failed image-recognition tasks point to a core concern about dealing with algorithms, namely, the question of agency, responsibility, and accountability to which I shall return in the next chapter. Sufice it to mention at this point the importance of the training data as another element for understanding the possibilities and limits of machine learning. As Barocas and Selbst point out, “what a model learns depends on the examples to which it has been exposed” (2016: 10). This raises an important question with regards to accusations of a “racist” algorithm: What was the initial data set that Google used to train its image recognition algorithms, and whose faces did it depict? What generalizable signals/paterns was it picking up on?\n\nTh e M u l tip l icity of A lgo rith ms\n\n27","previous_page":26,"next_page":28}