Understood as sets of instructions that direct the computer to perform a specific task, algorithms are essentially used to control the flow of actions and future events. Sometimes, the flow of events is more or less known in advance, as in the case of algorithms sorting an alphabetized list. More often than not in the world of machine learning, however, the outcome of events remains uncertain. Machine learning algorithms reduce this uncertainty by making predictions about the likelihoods of outcomes. Put diferently, machine learning is about strengthening the probability of some event happening, based on evolving information. This is also the principle of Bayes’ theorem: the “simple rule for updating your degree of belief in a hypothesis when you receive new evidence” (Domingos, 2015: 144).17 Following Wendy Chun, then, we might understand an algorithm as a “strategy, or a plan of action— based on interactions with unfolding events” (2011: 126). This implies that algorithms do not simply change with the event but are always in becoming since events are not static but unfolding. In the case of algorithmically driven sites such as Facebook, users are crucial to the development and maintenance of the underlying coding systems as they constantly feed the system with new data. As Mukund Narasimhan, a software engineer at Facebook, tellingly suggests: “Everything in Facebook is a work in progress.” The models Facebook uses to design the system are evolving because the data is changing. This means that the exact ways in which the algorithms work are also constantly tweaked by employees because of the fact that everything else changes (Narasimhan, 2011).
Algorithms do not simply change with the event; they also have the ability to change the event. In the era of big data and data mining, algorithms have the ability performatively to change the way events unfold or, at the very least, change their interpretation. A good example of this is the failed Google Flu Tracker, which was operative from September 2011 to August 2013. Often heralded as the prime example of what big data could do, Google’s Flu Tracker was designed to predict outbreaks of flu before they happened, based on mining data from their vast troves of search queries. According to Google, they “found a close relationship between how many people search for flu-related topics and how many people actually have flu symptoms” (Walsh, 2014). However, in February 2013, the Flu tracker made headlines because it turned out that it had predicted “more than double the proportion of doctor visits for influenza-like illness than the Centers for Disease Control and Prevention,” which had been used as a de facto predictor until then (Lazer et al., 2014: 1203). The fact that the tracker was dependent upon Google’s search algorithms played a significant part in skewing the results for flu trends. By recommending search terms to its users through its autocomplete feature, Google itself was producing the conditions it was trying to merely describe and predict. As Walsh (2014) put it: “If the data isn’t reflecting the world, how can it predict what will happen?”
28
IF . . . THEN