Algorithmic Rhetoric and Search Literacy John Jones West Virginia University [email_address] @johnmjones
algorithm = truth ?
metaphors for algorithms
1. autonomous machine 2. Rube Goldberg device 3. Mechanical Turk
autonomous machine
Rube Goldberg device
Mechanical Turk
1998, p. 1
2011, p. 66 "A site's ranking in Google's search results is  automatically determined by  computer algorithms using t...
"computer algorithms using  thousands of factors  to  calculate  a page's relevance to a given query."
1998, p. 12 "These types of personalized PageRanks are virtually immune to manipulation by commercial interests. For ...
NY Times , 2/26/2011
NY Times , 11/28/2010
NY Times , 2/25/2011
algorithms are authored
<ul><li>Image Sources </li></ul><ul><ul><li>Slide 2:
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Algorithmic Rhetoric and Search Literacy #hastac2011


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A search engine is rhetorical in that its design makes decisions about the importance of the information it indexes and serves to users. In this talk I examine some metaphors that help us understand how we think about algorithms.

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  • Our culture has been trained to separate technical and scientific knowledge from the contingencies of other forms of human knowledge, and to a great extent digital technologies have adopted this mantle of objectivity.
  • Yet, science isn&apos;t necessarily a strict 1-to-1 interpretation of the world; rather, science is an argument, and, just as science is a social process by which the world is enacted, the hardware and software that make up computer technology are socially enacted as well.
  • In other words, like all human efforts, the design and production of computer software and other digital technologies is influenced by the individuals who create it, their assumptions about the world, and, crucially, the ways in which those technologies enact the world and attempt to persuade their users that that enacting is the best or correct one.
  • To the extent that technologies are socially enacted attempts to persuade their users that they are true or correct, they are rhetorical. This talk is part of a larger project which focuses on a number of rhetorical aspects of search, but in this talk I want to discuss the persuasive functions of rhetoric.
  • Specifically: How did Google convince us that an algorithm can stand in for truth? That is, that it can deliver the best —or most correct—&amp;quot;answers&amp;quot; to our queries?
  • First, autonomous machines. With this metaphor, algorithms are considered to be independent of their human creators. Once set loose, they operate perfectly according to their parameters.
  • Or not.
  • A positive example of such an algorithm would be a calculator program which could utilize algorithms designed to return only a predefined set of answers. This metaphor emphasizes the independent, objective nature of algorithms; calculators don&apos;t make judgments, they calculate. We know when they make errors because they don&apos;t deal in contingencies, but these errors deal with issues directly connected to observable reality. In other words, they are right or wrong.
  • A second metaphor for algorithms is the Rube Goldberg device.
  • Rube Goldberg devices are incredibly complex ways of completing relatively simple tasks. Similarly, tasks that are simple to us—picking the music you like—are highly complex when enacted in software.
  • Consider the complexity involved with such algorithms as Pandora&apos;s stations or Amazon&apos;s recommendations. If we think of algorithms as Rube Goldberg devices we are recognizing that they are complex, meticulous technologies that require patience and exactitude to create.
  • The slightest misstep causes them to fail, and all that effort goes into what seem to be even the most trivial of tasks. In other words, the algorithm as Rube Goldberg device is complex, finicky, are easy to break and allow for little margin for error.
  • The final metaphor I wish to discuss is the Mechanical Turk.
  • The algorithm as Mechanical Turk is like the famous chess-playing &amp;quot;machine ” which relied on an operator hidden inside. When viewed in this way, algorithms are recognized to give the appearance of autonomous behavior, but maintaining this semblance of autonomous action requires frequent tinkering and is dependent on guidance from human controllers. Like the Mechanical Turk, this metaphor emphasizes that algorithms are mechanical, in that they have features that enhance the speed or accuracy of these human decisions, but they are not autonomous.
  • Google labors mightily to convince its users that its algorithm is an autonomous machine, or at least a Rube Goldberg device, when in reality it is a Mechanical Turk.
  • In their original paper on PageRank, the authors write that PageRank is &amp;quot;a method for rating Web pages objectively and mechanically&amp;quot; (1).
  • Vaidhyanathan explains how Google avoided problems with the French government over the display of anti-semitic websites in search results, by echoing this language, explaining how they relied on the &amp;quot;automatic&amp;quot; features of its search algorithm to convince regulators of the autonomous nature of these results.
  • However, even after Google removed the &amp;quot;automatic&amp;quot; from the description, it left a description designed to give the impression that the algorithm delivered mechanical or objective search results. This is reinforced by the reference to the &amp;quot;thousands of factors&amp;quot; and the &amp;quot;calculat[ions]&amp;quot; that determine the results.
  • One reason Google might encourage this connection is our cultural assumption that machines aren&apos;t capable of deception or other forms of obfuscation. That is, they are “ objective ” and don&apos;t lie. Garbage in, garbage out. They have inputs and outputs, but the quality of the output depends on the input, not on the algorithm that processes it. Such thinking reinforces the idea that algorithms are neutral in the process of producing information.
  • However, the algorithm itself is a contingent intervention in information habits that depends entirely on the biases and decisions made by its creators. That is, if garbage in garbage out applies to what we feed algorithms, it applies to algorithms themselves. This has always been the case.
  • In their 1998 paper on PageRank, the most widely-known component of Google&apos;s search engine, the authors wrote:
  • Of course, that&apos;s exactly what people did, and in response, the NY Times reported Google altered its algorithm to fight back against content farms designed to increase page views.
  • Not only can algorithms be manipulated, they make huge mistakes. Consider the case of and owner Vitaly Borker. Here, Borker found a flaw in the algorithm that apparently drove traffic to sites based on the frequency of online discussion, regardless of whether or not the discussion was positive (&amp;quot;I like;quot;) or negative (&amp;quot;the owner threatened to come to my house and sexually assault me&amp;quot;).
  • It is another example of a how a task that isn&apos;t a problem for us can be a problem for an algorithm, and therefore it requires human intervention. Of course, Google makes these interventions. According to a NYTimes article, Google makes &amp;quot;500 changes a year to the algorithm&amp;quot;, or more than one change every day.
  • Recognizing this intervention illustrates the extent to which Google is not involved in the search for perfect results: instead, they are engaged in a dialectical struggle with spammers about who gets to decide what counts as &amp;quot;good results.&amp;quot; Which returns us to the very notion of autonomous algorithms. Even they require some form of intervention. Calculators don&apos;t calculate autonomously, but rather require human input.
  • And their results are culturally influenced: my calculator doesn&apos;t calculate problems using base-6 or base-60 number systems.
  • In reality, the prototype for an autonomous machine algorithm is the infinite loop: it continues forever, without human intervention, entirely self-sustainable. But infinite loops are most often not useful sources of information. In reality, all algorithms are to some extent mechanical Turks. This doesn ’ t take away from their usefulness, but it does challenge us to think about their outputs differently, as contingent, rhetorical productions.
  • If algorithms are rhetorical, what benefit do we get from thinking about them in this way? That is, why does it matter? It helps us to realize that the results produced by algorithmic processes—including search engine results—are not objective truths, but rather are contingent on the assumptions and intentions of the authors who created them.
  • Because Google has done an excellent job of convincing its users that it&apos;s results are accurate—that is, that they are a stand in for reading actual site reviews, or other forms of information literacy—its continuing struggle to maintain this accuracy should give us pause.
  • This is not to say that what these algorithms produce can&apos;t be considered &amp;quot;true&amp;quot; or accurate, or that some algorithms don&apos;t produce superior results than others; rather, it is to say that algorithms are social constructions dependent on interaction with their creators and users, just like this Google doodle. As such, information seekers must examine the information available about these algorithms and the contingencies that produced them if they wish to come to reliable conclusions about that information.
  • Examining these contingencies is an important a factor in digital literacy, or what Howard Rheingold has called &amp;quot;infotention. ” It is as important as the examination of the publication history of printed texts—the name and affiliation of the author(s), who printed it, what form it was printed in, how it was edited, etc.—was for print literacy. (Lanham) Rhetoric was the original infotention, a way of thinking about the ways that information is packaged—in emotion, in style, in culture.
  • Understanding the extent of this cultural and social effect on an algorithm—including the authors, their presuppositions, the occasion for the text and the way in which the authors tried to adjust the text for the needs of a specific audience—helps information seekers to gauge the extent to which an algorithm can be bent to ends that make its original goals unrecognizable, and it can be extremely helpful tool for understanding the rhetorical impact of a particular set of search results.
  • Algorithmic Rhetoric and Search Literacy #hastac2011

    1. 1. Algorithmic Rhetoric and Search Literacy John Jones West Virginia University [email_address] @johnmjones
    2. 5. rhetoric
    3. 6. algorithm = truth ?
    4. 7. metaphors for algorithms
    5. 8. 1. autonomous machine 2. Rube Goldberg device 3. Mechanical Turk
    6. 9. autonomous machine
    7. 12. Rube Goldberg device
    8. 16. Mechanical Turk
    9. 19. 1998, p. 1
    10. 21. 2011, p. 66 &quot;A site's ranking in Google's search results is automatically determined by computer algorithms using thousands of factors to calculate a page's relevance to a given query&quot; was changed to &quot;A site's ranking in Google's search results relies heavily on computer algorithms using thousands of factors to calculate a page's relevance to a given query.&quot;
    11. 22. &quot;computer algorithms using thousands of factors to calculate a page's relevance to a given query.&quot;
    12. 25. 1998, p. 12 &quot;These types of personalized PageRanks are virtually immune to manipulation by commercial interests. For a page to get a high PageRank, it must convince an important page, or a lot of non-important pages to link to it. At worst, you can have manipulation in the form of buying advertisements(links) on important sites.&quot;
    13. 26. NY Times , 2/26/2011
    14. 27. NY Times , 11/28/2010
    15. 28. NY Times , 2/25/2011
    16. 32. algorithms are authored
    17. 37. <ul><li>Image Sources </li></ul><ul><ul><li>Slide 2: </li></ul></ul><ul><ul><li>Slide 4: </li></ul></ul><ul><ul><li>Slide 11: </li></ul></ul><ul><ul><li>Slide 12: </li></ul></ul><ul><ul><li>Slide 14: </li></ul></ul><ul><ul><li>Slide 15: </li></ul></ul><ul><ul><li>Slide 16: </li></ul></ul><ul><ul><li>Slide 18: </li></ul></ul><ul><ul><li>Slide 19: </li></ul></ul><ul><ul><li>Slide 24: </li></ul></ul><ul><ul><li>Slide 27: </li></ul></ul><ul><ul><li>Slide 28: </li></ul></ul><ul><ul><li>Slide 30: </li></ul></ul><ul><ul><li>Slide 31: </li></ul></ul><ul><ul><li>Slide 32: </li></ul></ul>