What are the main arguments in chapter 8, "Machine learning: The algorithmic production of knowledge," of Radical Technologies: The Design of Everyday Life?

In "Machine learning: The algorithmic production of knowledge," Adam Greenfield argues that machines will perform more and more everyday tasks in the future and that this will lead to serious problems in certain areas, particularly law enforcement and the relations between individuals and institutions.

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The main arguments from "Machine learning: The algorithmic production of knowledge" in Adam Greenfield's Radical Technologies: The Design of Everyday Life are as follows:

  • Algorithms are now just below the surface of many aspects of everyday life, determining such matters as whether you will be offered credit and which songs and films your streaming service is likely to recommend. The way in which they work and the aspects of life they cover are constantly evolving
  • Machine learning will allow the power of algorithms to be extended further, as they are taught to recognize patterns in the real world through neural networks. This will allow machines to perform many tasks previously open only to human beings
  • Many of the potential flaws in machine learning will not be serious in contexts such as social media. However, the use of machine learning in predictive policing carries serious risks for injustice and Orwellian surveillance of the innocent. This is a particular problem when the police are not looking for perpetrators of specific crimes but generally identifying those who are likely to be criminals
  • It is easy to teach machines to recognize objects, even very specific objects, but difficult to teach them to recognize concepts. The author asks, for instance, how one would teach an algorithm to identify poverty
  • One of the biggest problems in using algorithms is that you get results without knowing the reasons for those results. This is a particular difficulty in law enforcement. It also creates problems in areas such as loan applications and other institutional decisions
  • Beliefs about the future can become self-fulfilling when they drive action in the present. If people generally believe that the future is likely to be highly automated, it probably will be.
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