Algorithmic Media Need Democratic Methods: Why Publics Matter

Algorithms increasingly control the backbone of media and information systems. This control occurs deep within opaque technical systems far from the political attention capable of addressing its influence. It also challenges conventional public theory, because the technical operation of algorithms does not prompt the reflection and awareness necessary for forming publics. Informed public deliberation about algorithmic media requires new methods, or mediators, that translate their operations into something publicly tangible. Combining an examination of theoretical work from Science and Technology Studies (STS) with Communication Studies–grounded research into Internet traffic management practices, this article posits that mediating the issues raised by algorithmic media requires that we embrace democratic methods of Internet measurement. KeywoRdS Internet; Network policy; Technology theory; Research methods; Computer science RÉSUMÉ De plus en plus, les algorithmes gouvernent la base des médias et des systèmes d’information. Ce contrôle s’exerce au plus profond de systèmes techniques obscurs, loin de l’attention du monde politique et de responsables aptes à encadrer une telle influence. En outre, il remet en question la théorie classique du public. En effet, l’exploitation technique des algorithmes ne suscite pas la réflexion et la sensibilisation propres à éduquer le public. Ainsi, pour qu’ait lieu un débat éclairé, ouvert à tous, sur les médias algorithmiques, il faut privilégier de nouvelles méthodes, ou médiateurs, qui permettront de transposer les activités de ces médias en notions publiquement tangibles. La démarche proposée dans cet article associe l’étude de la communication, étayée par la recherche sur les pratiques de gestion du trafic Internet, à une analyse des travaux théoriques émanant de l’étude des sciences et des technologies. On y pose en principe que la résolution des questions soulevées par les médias algorithmiques passe par l’adoption de méthodes de mesure Internet démocratiques. MoTS CLÉS Internet; Politique de réseau; La théorie de la technologie; Les méthodes de recherche; Informatique Introduction we increasingly hear that communication has become more algorithmic (Napoli, 2013) or, more specifically, that communication infrastructures depend on computational routines to control the information they acquire, produce, and diffuse. But what is an 598 Canadian Journal of Communication, Vol 39 (4) algorithm? why does it matter? How are we to study it? Algorithms refer to the components of software that make up information and communication infrastructures. If software “is the set of instructions that direct a computer to do a specific task” (Ceruzzi, 1998, p. 80), then algorithms are the instructions. They “do things and their syntax embodies a command structure to enable this to happen” (Goffey, 2008, p. 17). doing things, following Beniger (1986), involves processing an input toward an output. one algorithm informs another, creating a continuous modulation that Gilles deleuze (1992) identified as distinct to contemporary power. Algorithmic media emphasize these computational routines as general characteristics—as overall processes that dynamically adjust outputs and inputs. Algorithms function as control technologies in all sorts of media and information systems, dynamically modifying content and function through these programmed routines. Search engines and social media platforms perpetually rank user-generated content through algorithms. Google depends on its PageRank algorithm and other propriety code to tailor Google results according to the searcher (Feuz, Fuller, & Stalder, 2011). Facebook relies on algorithms to populate users’ Newsfeeds (Bucher, 2012). youTube depends on algorithms—in the form of its ContentId system—to compare video uploads to known copyrighted works and, subsequently, flag matches (dayal, 2012). delegating copyright enforcement to automated high-capacity algorithms allows it to handle 72 hours of uploaded video material every minute. The sleeping policeman has become the algorithmic cop. Algorithmic control has significant social consequences, not the least of which is the creating of associations that resemble publics (Gillespie, 2014). when a customer finds an app in the Google Play Store, algorithms recommend similar ones, but these recommendations may make troubling assumptions about users. Mike Ananny (2011) found one such troubling assumption when examining the Grindr app, an online dating app for gay men. when looking at this particular app, the Google Play Store recommended a Sex offender Search tool as another app to download. why, Ananny asked, would the app store’s algorithms link sex offenders with Grindr? His question provoked a critical examination of the reasons for the linking of these two apps in a manner that implicitly associated being gay with being a sex offender. In Canada, algorithms became a major policy issue in 2008 when Internet service providers (ISPs) were found to be using Internet routing algorithms—or Internet traffic management practices—to limit peer-to-peer file-sharing through their networks. These practices demonstrated a new-found capacity for detecting specific applications and constraining their bandwidth consumption on residential broadband networks. The delegating of network management to algorithms sparked a public controversy about how to regulate their use. In response to these concerns, the Canadian Radiotelevision and Telecommunications Commission (CRTC) formulated Telecom Regulatory Policy CRTC 2009-657, which sought to balance the interests of ISPs in managing traffic on their networks with those of Internet users. Given the shared interest of Communication Studies and Science and Technology Studies (STS) in media and information technologies as “the backbone of social, economic and cultural life in many societies” (Boczkowski & Lievrouw, 2008, p. 951), scholars must attend to algorithms precisely because media and information systems cannot be wholly understood without a sense of the underlying routines that regulate them.1 The concept of algorithmic media seeks to avoid overemphasizing the importance of any single algorithm in a system or of written code.2 This is because algorithms only have an influence when they are running. No one algorithm is constitutive of the Internet or any algorithmic media. Information and communication media are systems composed of algorithms that cooperate and compete with one another (McKelvey, 2010; van Schewick, 2010). The concept, then, encourages looking at these many processes and how they interact. As its stands now, algorithms leave little room for debate about the forms of cultural, economic, and social control they exert. yet, as noted by Tarleton Gillespie (2014), “insights into the inner workings of information algorithms is a form of power, vital to participating in public discourse, essential to achieving visibility online, constitutive of credibility and the opportunities that follow” (p. 185). Thus, researchers must address not only algorithmic media, but also its general condition of imperceptibility. If algorithms matter—and I believe they do—they require greater democratic accountability. A democratic response to algorithmic media requires translating the fleeting operations of software system routines into knowledge that is conducive with democratic debate. However, methods must be created to study algorithmic media. In this article I argue that democratic inquiry as imagined by John dewey (1927, 1990) offers a promising means for doing so. democratic inquiry, one of the many ways to describe dewey’s work, refers to a belief that those affected by particular issues—what he calls publics—have a direct stake in contributing to resolving them. when applied to contemporary information and communication environments, the tenets of this perspective suggest that publics offer a valuable means of generating knowledge about algorithmic media. For the most part, algorithmic media do not capture the public’s attention. Their operation happens instantaneously within opaque technical systems. Their running code is often imperceptible and hardly ever known to its users. A packet does not arrive with an explanation of what decisions it took along its journey. As such, algorithmic media behave differently and have different subjectivities than the media that dewey associated with early publics theory. The perspective advanced in the pages that follow examines the relation between algorithms and publics theory. drawing on work from Communication Studies and Science and Technology Studies, the approach presented juxtaposes ideas from these two fields in working toward a model of democratic inquiry for algorithmic media. while the former attends to the specifics of media and information systems, the latter has discussed publics primarily in relation to technical and/or scientific controversy. The underlying hypothesis guiding the discussion set out below is that democratic inquiry contributes to identifying and remembering the operations of control by algorithms, thereby allowing for more tangible political and policy debates. New media, new control, old questions Processes of command, control, communications and intelligence (C3I)) have long been a subject of shared concern for both critical communications and media studies, McKelvey Algorithmic Media Need Democratic Methods 599