Algorithms as a Social Concern
Incidents like the Google Flu tracker or the more recent examples of data and algorithmic discrimination outlined above have arguably encouraged many more social scientists and humanities scholars to explore the ethical, political, and social implications that these digital infrastructures and systems have. Since the first cycle of hype and optimism concerning the rise of big data seems to be fading gradually, we are now seeing a growing scholarly interest in what Tarleton Gillespie and Nick Seaver have termed “critical algorithm studies.” Over the past decade or so, work spanning sociology, anthropology, science and technology studies, geography, communication, media studies, and legal studies has started to focus critical atention not just on software but algorithms more specifically (Amoore, 2009; Ananny, 2016; Beer, 2009; Cheney-Lippold, 2011, Diakopoulos, 2015; Gillespie, 2014; Introna, 2016; Karppi & Crawford, 2016; Lenglet, 2011; Mackenzie, 2015; McKelvey, 2014; Seaver, 2013; Striphas, 2015; Wilf, 2013; Ziewitz, 2016). Whereas computer scientists typically focus on designing eficient algorithms, a sociological or cultural approach to algorithms is starting to emerge, focusing on what algorithms are actually doing as part of situated practices. Indeed, an algorithmic logic of information processing and dissemination has become pervasive in such fields as finance (Mackenzie, 2015; Pasquale, 2015), transportation (Kitchin & Dodge, 2011), the travel sector (Orlikowski & Scot, 2015), higher education (Introna, 2011; Williamson, 2015), journalism (Anderson, 2013; Diakopoulos, 2015; Dörr, 2016), security (Amoore, 2013; Cheney-Lippold, 2016), surveillance (Braverman, 2014; Introna & Wood, 2004), popular culture (Beer, 2013), and the media industry more generally (Hallinan & Striphas, 2016; Napoli, 2014).18 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. As Gillespie (2016a) suggests, critical algorithm scholars are often more concerned with the algorithm as an adjective, understood as the social phenomena that are driven by and commited to algorithmic systems.
While the growing concern over algorithms may be a relatively recent phenomenon in the context of the social sciences and humanities—in part, spurred by the rise of big data—the underlying social concerns have a much longer history. The more pervasive use of algorithms in a wide range of societal sectors and institutions should be seen along a much longer continuum of scholarship concerned with the intersection of technology and society, the social history of computing, and related fields such as new media studies, software studies, platform studies, STS, and humancomputer interaction (HCI). Work in these domains has contributed knowledge about the power and politics of computing technologies, software systems, and information infrastructures relevant to our present concern with the role of algorithms in the contemporary media landscape. Take, for example, the growing interest in software as a cultural object of study investigated in the field of software studies (Fuller, 2008). During the past decade or so, social scientists and humanities scholars have called for an expanded understanding of code that extends significantly beyond its technical definitions (Berry, 2011; Fuller, 2008; Kitchin & Dodge, 2011; Mackenzie, 2006). Software studies can be understood as a cultural studies approach to the “stuf of software” (Fuller, 2008), where what counts as “stuf” remains relatively open to interpretation. This conceptual openness is, perhaps, what distinguishes software studies from computer science approaches since what counts as software is seen as a “shifting nexus of relations, forms and practices” (Mackenzie, 2006: 19). This is not to say that scholars interested in software as a cultural object of study dismiss the importance of materiality or technical operations, quite the contrary. While calling for an expanded understanding of the meaning and significance of software as part of everyday life, scholars within software studies also maintain a high level of sensibility toward the technical and functional dimensions of software. In his book Protocol, Alexander Galloway makes the case that “it is not only worthwhile, but also necessary to have a technical as well as theoretical understanding of any given technology” (2004: xiii). In order to understand power relationships at play in the “control society,” Galloway argues that it is crucial to start with the questions of how technology (in this case, protocols such as HTP) works and who it works for. In fact, not addressing the technical details of software or algorithms as part of a sociological or critical inquiry is seen as problematic (Rieder, 2017: 101). While media scholars difer in their assessment of how necessary coding skills are to a critical understanding of software and algorithms, some technical knowledge is clearly desirable.19
Th e M u l tip l icity of A lgo rith ms
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