Introduction
Over the last few decades, the discussions around the advances associated with the internet and social media have gained ground within the field of reflection on technology and society. Even though some authors drew attention throughout the 20th century to the need to think about the consequences that unlimited techno-scientific development could have on society (Heidegger 2003, Winner 1980), in the early years of the 21st century these discussions have become more visible due to the fact that Information and Communication Technologies (ICT) permeate all areas of contemporary life.
Usually the reflections around ICTs and their impact swing between exaltation, glorification and celebration, on the one hand; and the contempt, disapproval and revulsion on the other. Namely, between techno-optimists and techno-pessimists, between technophiles and technophobes (Breton 2011, Broussard 2018, Sfez 2005). The former are amazed by the possibilities of technology to solve all the problems facing humanity and the advance towards more democratic, egalitarian, creative, collaborative, intelligent, tolerant and prosperous societies. Meanwhile, the latter see technological innovations as a threat to human beings, who will be replaced and governed by machines, ending not only their freedom and autonomy, but also rendering them useless and unnecessary.
It seems then that in order to intervene in this discussion, one would have to declare to be either a lover or enemy of technology since even halfway positions are frowned upon:
“If the authors declare themselves enemies of technology, they are reactionary, retrograde, archaic sclerotic, unable to accept changes […]. But if, on the contrary, the authors defend technical culture, they turn out to be boastful, naive, unconscious, without a true culture […] if the author tries to weigh things up and maintain honest moderation, of good law, he lacks ideas, he doesn’t know how to choose, he’s a softie” (Sfez 2005).
Phillippe Breton (2011), in a similar sense, expresses that the division between technophobes and technophiles is a fallacy that minimizes and simplifies the debates around technological developments.
Although it seems contradictory, technophiles and technophobes agree on one point: that the best or the worst of the future depends solely and exclusively on technology, hence both are technological determinists. This determinism prevents the understanding of the political, economic and cultural impacts of technology from complexity.
In that context, this chapter proposes a discussion and a critical approach to certain technological developments, specifically, to those related to algorithms and their implications on people’s daily lives. It does not seek to indicate if algorithms are good or bad, but to instead highlight that they increasingly play a more important role in the way in which human beings relate to each other and experience the world, in the manner and sources from which we build the senses and meanings that guide our behaviours in both online and offline environments. Thus, it shows how algorithms affect and shape our culture.
The approach to what we will call algorithmic culture will be carried out from a phenomenological and transdisciplinary perspective. “From a phenomenological perspective, approaching algorithms is about being attentive to the ways in which social actors develop more or less reflexive relationships to the systems they are using and how those encounters, in turn, shape online experience” (Bucher 2018). It is transdisciplinary because it takes ideas from some 20th century thinkers of the technique, as well as contributions from philosophy, anthropology, communication and sociology that have addressed the contemporary relationship between technology and society.
At this point, it is important to clarify that the creation and emergence of a new culture is not advocated here, as the promoters of cyber-culture once prophesised (Lévy 2007). The central argument is that contemporary culture continues to be influenced by power relations, inequalities and inequities that are reinforced and/or legitimized through algorithms.
Algorithms: Computing Machines of the 21st Century
Since the Enlightenment and modernity, the desire of humans to calculate, control and predict the world has become more intense (Mayer-Schönberger and Kenneth 2013, Han, 2014, Crosby 1998). According to the philosopher Yuk Hui, “our way of speaking about progress since the 18th century is marked by the desire to measure, calculate, and dominate” (2020). This was described by Martin Heidegger as “calculative thinking” (2003), thus, part of the developments of the last two centuries in science and technology have been oriented to this purpose, in the hope that a calculable world would be more controllable and, at the same time, better, more efficient, and more productive (Rosa 2020).
Techno-science of the 20th century concentrated itself on developing machines, theories and instruments that would help quantify the world, not only to understand and control it, but also to solve all its problems (Morozov 2015). That was one of the cybernetic goals of the mathematician Nobert Wiener in the middle of the last century (Rid 2016); hereafter Heidegger has seen in cybernetics the most advanced state of calculative thinking.
After World War II, when in addition to cybernetics, the field of computing and Artificial Intelligence was developed, algorithms became part of the scientific language of the time (Berlinski 2000). Despite this, according to some scholars, the term algorithm dates back to the 9th century associated with the Persian mathematician Abdullah Muhammad ibn Musa Al-Khwarizmi (Steiner 2012). However, its use in the field of mathematics did not spread until at least approximately the 17th century, and in the field of computing from the second half of the 20th century.
For these reasons, the starting point must be to strengthen our definition of an algorithm. Put simply, it is like a series of instructions and concrete and finite steps to obtain a particular result (Bucher 2018, Domingos 2015, Finn 2018, O’Neil 2018, Steiner 2012). “An algorithm is an effective procedure, a way of getting something done in a finite number of discrete steps. Classical mathematics is, in part, the study of certain algorithms” (Berlinski, 2000). Algorithms did not appear with informational languages, but it was instead thanks to the development of computer science that their use spread and popularized to occupy the space that they currently have in the contemporary world.
The instructions computational algorithms follow are limited and finite, and are oriented to achieve a concrete result, to offer a solution to the many possibilities that can be obtained. Although an algorithm is a series of instructions, not all algorithms are the same nor do they produce the same results. It all depends on the tasks for which they were programmed and the data they process to fulfil it. According to Dominique Cardon (2016, 2018), there are popularity, authority, reputation and prediction algorithms.
This is the most common and widespread definition, even outside the field of mathematics and computer science; therefore, it is also pertinent to talk about algorithmic culture, because here we are interested in algorithms not only for what they are, but also for what they do: “Social scientists and humanities scholars are not primarily concerned with the technical details of algorithms or their underlying systems but, rather, with the meanings and implications that algorithmic systems may have” (Bucher 2018).
In that sense, algorithms should not be understood only as mathematical artefacts, but rather something cultural. “An algorithm is a perfectly well-defined mathematical object; but it is as well a human artefact, and so an expression of human needs” (Berlinski 2000). No matter how much they are fomented and promoted as objective and neutral tools that only seek to optimize certain processes and for decision-making, algorithms must be understood as cultural artefacts, as producers and reproducers of culture (Gillespie 2016), even if they go unnoticed and seem invisible, algorithms increasingly intervene in the lives of people and societies at large:
“Algorithms are in every nook and cranny of civilization. They are woven into the fabric of everyday life. They’re not just in your cell phone or your laptop but also in your car, your house, your appliances, and your toys. Your bank is a gigantic tangle of algorithms, with humans turning the knobs here and there. Algorithms schedule flights and then, the airplanes. Algorithms run factories, trade and route goods, cash the proceeds, and keep records” (Domingos 2015).
The fact that today algorithms play a part in practically all aspects of contemporary life makes them an object of cultural interest, therefore, an object of reflection for the social and human sciences (Manovich 2017).
Despite the fact that algorithms intervene in more and more aspects of everyday life, few know how they function and how they act; however, “even if we do not know what they are and that they are invisible to us, our daily life is increasingly related and conditioned by algorithms” (García and Valle 2020). But aside from the fact that they increasingly impact on culture, algorithms must be understood as cultural artefacts because they are elaborated by people (programmers) who cannot abstract themselves from the contexts and interests under which they elaborate and program them, algorithms carry the print of their creators (Berlinski 2000). For this reason, various female authors have shown that algorithms can become sexist, classist and racist (O’Neil 2018, Eubanks 2021, Noble 2018, Broussard 2018).
Consequently, algorithms reinforce and reproduce historical and hegemonic cultural values such as those associated with gender, social class, and skin colour, among others. “Race, sex, sexual orientation, religion, ability, and other categories have not disappeared online but have become more complicated” (DeNardis 2020). Thus, it’s important to approach algorithms as cultural objects, since to look at them simply as useful mathematical processes for the optimization of certain processes, make the consequences of their applications invisible: “But the point is not whether some people benefit. It’s that so many suffer. These models, powered by algorithms, slam doors in the face of millions of people, often for the flimsiest of reasons, and offer no appeal. They’re unfair” (O’Neil 2018).
A central aspect for the operation and programming of algorithms is the data. With the advancement of the internet and the appearance of applications and platforms (Van Dijck et al. 2018), different sectors and social and individual activities have been digitized, which has impacted not only the way in which they are developed, but the interests behind them. Parallel to this digitization process, a new resource and source of income emerged that is generating changes in almost all sectors of the economy and social life: data. “From representing a peripheral aspect of businesses, data increasingly became a central resource. In the early years of the century it was hardly clear, however, that data would become the raw material to jumpstart a major shift in capitalism” (Srnicek 2018).
The production, capture, storage and analysis of data is known as big data; and for more than a decade in economic, political and academic circles people have been referring to them as the revolution of the 21st century (Cardon 2018, Mayer-Schonberger and Cukier 2013, Stephens-Davidowitz 2019). This increase in the ability to produce, ...
