If...Then Algorithmic Power and Politics · 第 161 页

Chapter 1

1. Facebook’s tagline as of June 2017.

1. Netflix’s tagline as of June 2017.

Chapter 2

1. htp://cs-exhibitions.uni-klu.ac.at/index.php?id=193

1. According to Hromkovič, Leibniz was the first philosopher who conceptualized mathematics as an “instrument to automatize the intellectual work of humans. One expresses a part of reality as a mathematical model, and model, and then one calculates using arithmetic” (2015: 274).

1. Not all algorithms depend on “if. . . then” statements. Other control structures include “while” or “for” loops.

1. Elegance, as first proposed by Donald Knuth in Literate Programming (1984), can be measured by four criteria: the leanness of the code, the clarity with which the problem is defined, the sparseness of the use of resources such as time and processor cycles, and implementation in the most suitable language on the most suitable system for its execution.

1. Some of the most basic data structures include the array, the record, the set, and the sequence, of which array is probably the most widely used. More complicated structures include: lists, rings, trees, and graphs (Wirth, 1985: 13).

1. For more on the history, cultures, and technical aspects of databases, see Bucher (2016), Codd (1970), Driscoll (2012), Dourish (2014), Manovich (1999), and Wade & Chamberlin (2012).

1. In a program writen in the programming language C, the diferent steps can usefully be illustrated by the program’s file-naming convention. The source code file ends in “.c,” the object code ends in “.obj,” and the executable files end in “.exe.”

1. While it is beyond the scope of this chapter to lay out how the extremely complex fields of cognitive science or children’s cognitive development understand human learning, it may sufice to say that “many machine-learning researchers take inspiration from these fields” (Domingos, 2015: 204).

1. A fourth category of machine learning that is usually listed is reinforcement learning, which is about learning in situ as the system interacts with a dynamic environment, for example, in the case of self-driving cars.

1. For a good overview of the technical details on how data mining and machine learning work, see Barocas & Selbst (2016). They provide an excellent description of the diferent processes and steps in data mining, including defining the target variable, labeling and collecting the training data, selecting features, and making decisions on the basis of the resulting model.

Notes

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