Яανeη对《Media Technologies》的笔记(2)

Algorithms play an increasingly important role in selecting what information is considered most relevant to us, a crucial feature of our participation in public life. Search engines help us navigate massive databases of information, or the entire web. Recommendation algorithms map our preferences against others, suggesting new or forgotten bits of culture for us to encounter. Algorithms manage our interactions on social networking sites, highlighting the news of one friend while excluding another’s. Algorithms designed to calculate what is “hot” or “trending” or “most discussed” skim the cream from the seemingly boundless chatter that’s on offer. Together, these algorithms not only help us find information, they also provide a means to know what there is to know and how to know it, to participate in social and political discourse, and to familiarize ourselves with the publics in which we participate. They are now a key logic governing the flows of information on which we depend, with the “power to enable and assign meaningfulness, managing how information is perceived by users, the ‘distribution of the sensible.’”

But as we have embraced computational tools as our primary media of expression, and have made not just mathematics but all information digital, we are subjecting human discourse and knowledge to these procedural logics that undergird all computation. And there are specific implications when we use algorithms to select what is most relevant from a corpus of data composed of traces of our activities, preferences, and expressions.

These algorithms, which I'll call public relevance algorithms, are—by the very same mathematical procedures—producing and certifying knowledge. The algorithmic assessment of information, then, represents a particular knowledge logic, one built on specific presumptions about what knowledge is and how one should identify its most relevant components. That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the word of God. What we need is an interrogation of algorithms as a key feature of our information ecosystem (Anderson 2011), and of the cultural forms emerging in their shadows (Striphas 2010), with a close attention to where and in what ways the introduction of algorithms into human knowledge practices may have political ramifications. This essay is a conceptual map to do just that. I will highlight six dimensions of public relevance algorithms that have political valence:

1. Patterns of inclusion: the choices behind what makes it into an index in the first place, what is excluded, and how data is made algorithm ready.

2. Cycles of anticipation: the implications of algorithm providers’ attempts to thoroughly know and predict their users, and how the conclusions they draw can matter.

3. The evaluation of relevance: the criteria by which algorithms determine what is relevant, how those criteria are obscured from us, and how they enact political choices about appropriate and legitimate knowledge.

4. The promise of algorithmic objectivity: the way the technical character of the algorithm is positioned as an assurance of impartiality, and how that claim is maintained in the face of controversy.

5. Entanglement with practice: how users reshape their practices to suit the algorithms they depend on, and how they can turn algorithms into terrains for political contest, sometimes even to interrogate the politics of the algorithm itself.

6. The production of calculated publics: how the algorithmic presentation of publics back to themselves shape a public’s sense of itself, and who is best positioned to benefit from that knowledge.

Algorithms are inert, meaningless machines until paired with databases on which to function. A sociological inquiry into an algorithm must always grapple with the databases to which it is wedded; failing to do so would be akin to studying what was said at a public protest, while failing to notice that some speakers had been stopped at the park gates. For users, algorithms and databases are conceptually conjoined: users typically treat them as a single, working apparatus. And in the eyes of the market, the creators of the database and the providers of the algorithm are often one and the same, or are working in economic and often ideological concert.

Search algorithms determine what to serve up based on input from the user. But most platforms now make it their business to know much, much more about the user than the query she just entered. Sites hope to anticipate the user at the moment the algorithm is called on, which requires knowledge of that user gleaned at that instant, knowledge of that user already gathered, and knowledge of users estimated to be statistically and demographically like them (Beer 2009)—drawing together what Stalder and Mayer (2009) call the “second index.” If broadcasters were providing not just content to audiences but also audiences to advertisers (Smythe 2001), digital providers are not just providing information to users but also users to their algorithms. And algorithms are made and remade in every instance of their use because every click, every query, changes the tool incrementally.

But algorithms are not always about exhaustive prediction; sometimes they are about sufficient approximation. Perhaps just as important as the surveillance of users are the conclusions providers are willing to draw based on relatively little information about them.

Beyond knowing the personal and the demographic details about each user, information providers conduct a great deal of research trying to understand, and then operationalize, how humans habitually seek, engage with, and digest information.

Understanding algorithms and their impact on public discourse, then, requires thinking not simply about how they work, where they are deployed, or what animates them financially. This is not simply a call to unveil their inner workings and spotlight their implicit criteria. It is a sociological inquiry that does not interest the providers of these algorithms, who are not always in the best position to even ask. It requires examining why algorithms are being looked to as a credible knowledge logic, how they fall apart and are repaired when they come in contact with the ebb and flow of public discourse, and where political assumptions might not only be etched into their design, but also constitutive of their widespread use and legitimacy.

A sociological inquiry into algorithms should aspire to reveal the complex workings of this knowledge machine, both the process by which it chooses information for users and the social process by which it is made into a legitimate system. But there may be something, in the end, impenetrable about algorithms. They are designed to work without human intervention, they are deliberately obfuscated, and they work with information on a scale that is hard to comprehend (at least without other algorithmic tools). And perhaps more than that, we want relief from the duty of being skeptical about information we cannot ever assure for certain. These mechanisms by which we settle (if not resolve) this problem, then, are solutions we cannot merely rely on, but must believe in.

Moreover, this “user turn” was reinforced by critical and cultural media studies and its socialized, culture-driven approach to media analysis, including a central focus on media institutions and power (see the section to follow on Critical/Cultural Media Studies). Following the lead of Raymond Williams (1974), these writers cast people as engaged, critical, “active audiences” rather than passive receivers or consumers of mass media messages and content (Ang 1990; Jensen and Rosengren 1990; Livingstone 2004; Morley 1993)—a perspective that seemed to apply just as well to “active,” and interactive, Internet users. Consequently, to a great extent the classical “effects” or “impacts” viewpoint in new media research gave way to studies with a broadly social constructivist perspective and an emphasis on social shaping, the shared or negotiated meaning of technologies, user studies, and technological systems as products and representations of culture. The question was no longer what communication technologies or media do to people, but rather, how people appropriate, understand, make sense and continuously reconstruct them. Communication technologies—at once resources for and manifestations of communication, meaning, and culture—seemed to epitomize the articulation between the technical and the social. As the means for expression, interaction, and cultural production, and cultural expressions and productions in themselves, media and communication technologies could be seen as both “cultural material and material culture” (Boczkowski and Lievrouw 2008, 955)—but the socially constructed nature of technology took priority. Ultimately, the move from technological determinism to social constructivism was a pivotal development in communication and media research in the 1990s; by the 2000s a strong form of social/cultural determinism had “become the dominant perspective in new media studies”

Thus the main purpose of the following discussion is to explore how the material nature of communication and media technologies has been conceptualized at the intersection of STS and communication studies. On one hand, two decades of debates have encouraged STS researchers to theorize technology as simultaneously and inextricably social and material, to see both aspects as co-determining, and to attend as closely to the physical design and configuration of objects as to the beliefs and social circumstances of their creators and users. Among communication scholars, on the other hand, the physical, material features of technology are still more likely to be explained as outcomes or products of abstract social forces, cultural discourses, or economic logics—or as all of these together. Only secondarily, if at all, are material artifacts and devices themselves considered to have anything like a parallel power to influence human action, society, and culture, or do scholars insist as strongly on the influence or “agency” of material objects and artifacts as that of human actors and institutions. Even where devices themselves are the main focus, as in some social histories of media technologies, they are often analyzed from interpretive points of view that privilege the signifying or discursive aspects of technological artifacts over the concrete, embodied quality of crafting and using them.

Materiality itself is a complex, multidimensional idea, and open to a variety of interpretations, emphases, and disciplinary assumptions. It has been invoked to describe phenomena as diverse as the economic and institutional power of media industries and markets, cultural practices like art making and religious rituals, and the microscale utterances and turn taking of speech and interpersonal interaction. Obviously all these levels and shades of meaning cannot be adequately addressed in a single chapter. Therefore, for the purposes of the present discussion I use a much narrower conception based on the three-part definition noted previously, where communication technology is conceived as the articulation of artifacts, practices, and social arrangements (Lievrouw and Livingstone 2006). All three elements are interwoven and mutually determining, but here I focus on the first element, to define materiality as the physical character and existence of objects and artifacts that makes them useful and usable for certain purposes under particular conditions. Such a definition foregrounds the materiality of artifacts, of things, not to deny the materiality of practices or social or institutional forms, but to consider how communication technology studies might also engage more fully with the materiality of the devices themselves without necessarily opening itself to charges of simple technological determinism.

Actor Networks Perhaps the best-known approach to technology studies among researchers outside STS is associated with Michel Callon, Bruno Latour, John Law, and their colleagues (Latour 2005; Law and Hassard 1999). Actor-network theory (ANT) maps relationships among material entities and artifacts, human actors, and the ideas or symbols associated with them as “heterogeneous” and open sociotechnical networks. Human actors and material artifacts or actants are nodes in interlinked webs of relations, with the ability to exert multiplex influence on other relationships and nodes in the network. The actor-network perspective resists the “purification” of issues or practices into singular categories like nature, science, politics, culture, or even place: all these categories, and more, are necessarily and simultaneously implicated in a given network (Latour 1993). The insistence on heterogeneity, especially the idea that material artifacts and objects can be agents within networks of relations among humans and knowledge, is perhaps the (P. 30 ) most controversial aspect of ANT, and most firmly places the materiality of things at the center of the theory.

Co-Production A fourth concept not only suggests that science and technology are simultaneously material and social, but also aims to elaborate the dynamics of the relationship, especially as a means for confronting authority and intervening in technological controversies and policy. According to Sheila Jasanoff, its most notable proponent, co-production is the simultaneous creation of knowledge and artifacts/practices, which actually constitutes social life: “the ways in which we know and represent the world (both nature and society) are inseparable from the ways in which we choose to live in it. Knowledge and its material embodiments are at once products of social work and constitutive of forms of social life”

However, unlike autonomous technology, and like technological momentum and actor networks, co-production regards science and technology as heterogeneous by definition: “scientific knowledge, in particular … both embeds and is embedded in social practices, identities, norms, conventions, discourses, instruments, and institutions … the same can be said even more forcefully of technology”

Jasanoff suggests that co-production can be found in four “sites” or processes: making identities, making institutions, making discourses, and making representations. In the social sciences, cultural studies, and the humanities, these phenomena are often treated as abstract concepts that are independent of their particular manifestations or physical forms. Jasanoff, however, insists that such material forms actually constitute identity, institutions, discourses, and representations. Identity, for example, is manifested in modes of dress, speech, food, kinship relationships, and so on. Discourses are articulated in political campaigns, architecture, publishing, conversation, stock prices, or monthly bills for Internet services. Institutions exist in the form of legal codes, educational practices, configurations of space, or social roles. Representations convert concepts into concrete form, as when terrorism is equated with an image of a hooded street fighter, or genetically modified crops are labeled “Frankenfood.” In each case the physical forms and objects constitute the ideas in question; such manifestations can be parsed and analyzed, and new ones crafted as needed.

These four sketches allow us to consider how materiality, particularly with respect to technology artifacts, figures across them all. First, and most obviously, all four frameworks attempt to theorize the physical character, presence, or durability of technological devices and objects as a factor on par with social, cultural, or political negotiation and construction. They do not deny or downplay social construction, but attempt to join the material and the social as essential, and essentially co-determining, elements (P. 32 ) in technological change. Second, all the frameworks, to varying degrees, emphasize the heterogeneity of technology, that is, as a multifaceted and dynamic phenomenon that entails and imbricates not just artifacts, social practices and relationships, and knowledge, but a variety of all these elements. If, as Latour and his colleagues say, analysts must “follow the actors,” this means dealing with a host of interconnected artifacts and social formations as well as people (Latour 1987). Third, all four frameworks address the stabilization or standardization of material objects as a key mechanism for explaining their influence. Social studies of technology often highlight technological innovation and the origins of particular devices or systems; these early stages, after all, are more likely to be dominated by social construction processes, negotiations, and contingent, contested meanings and understandings. However, the frameworks presented here pay as much attention to technological stabilization and routinization processes in which the artifacts themselves (whether due to habit, physical inertia or scale, sunk costs, network effects, or reliability) acquire the capacity to shape social practices and organize action.