While only very recently have scholars begun to address the complex rhetorical strategies behind algorithms(see Ingraham, 2014), to discuss them I would like to pull one of the oldest rhetorical tricks out of the bag, the dissoi logoi. Most electronic media users, I suspect, will think of algorithms as a vehicle for marketing, but in opposition I would like to describe another model, a crowd-sourced algorithm for linguistic and literary study.

The marriage of big data with sophisticated algorithms has spawned an electronic Big Brother that few of us have been able to ignore as he continuously looks over our shoulder. The press have even celebrated algorithms that subtly observe our electronic behavior, often triangulate it with data on similar users, crunch a vast record of credit card purchases, and come to accurate conclusions: perhaps most celebrated, the Algorithm will know when a woman is pregnant before her family will (see Hill, 2012). Suffice it to say here, Aristotle would easily understand that an enterprise cleverly attaining insight into individual motivations (e.g., instinct to shop for a crib – Rhetoric 2.2-11) in order to deliberately craft a sales pitch (Target famously had its algorithm mix non-pregnant-woman ads into fliers to disguise their real purpose – Rhetoric 2.12-17) that will persuade someone to a specific course of action (buying crib, car seat, diapers, etc., at Target – Aristotle is reserved on this part, but see Rhetoric 3.19) is fundamentally rhetorical. The algorithm, in good rhetorical style, learns all it can about the audience, ponders the best way to persuade it, and then makes its well-researched pitch. And as with the majority of good rhetoric over the ages (pace Plato and Aristotle), it performs all the stages of persuasion not only without any complicit consent of the audience, but under the well-rewarded intention that the audience should remain as unconscious of the whole procedure as possible. This algorithmic rhetoric should be called in blunt terms sophistic. Such forms of persuasion are so ubiquitous, and the goal of selling car seats so benign, that most audiences happily direct their attention away from this form of persuasion.

Voluntarily crowd-sourced algorithms do the opposite. Let’s take the classical example of dependency treebanking as used in the award winning Perseus Project, an algorithm that recruits expert users to mark up grammatical information in a text with the goal of creating scientifically accurate dictionaries, grammars, and even tables of references and allusions (see The Ancient Greek and Latin Dependency Treebank and Bamman and Crane, 2011). So when Target’s algorithm peeks down a dark alley to figure out what you are buying, the treebank calls you over to volunteer to help paint a mural on the wall. When Target asks your acquaintances behind your back what you really like to buy, treebank tells you that your friends are going to double check your work for accuracy. When Target analyzes the information it snuck from you to create a flyer that will deceive you from their real purpose, treebank analyzes the information it elicited from your hard work to illuminate new meanings in the text for you.

So much for the first part of the dissoi logoi, the stark opposition. The next step needs to be an effort at synthesis. On the one hand it should come as no surprise that voluntary crowdsourcing algorithms have become suddenly popular in the corporate world. Microsoft, for instance, has made a recent and concerted push in this direction, and we won’t be surprised if these algorithms start to get sophistic and make lots of money. On the other hand, very few teachers of ancient Greek and Latin literature will resist for long a sophistic algorithm that would, for instance, surreptitiously observe students reading an electronic version of the Iliad, triangulate their habits with the immense data on reading habits we have that use English-language treebank projects, and eventually find covert ways to seduce them into reading more effectively and pleasurably (see, for instance, R. Levy, K. Bicknell, T. Slattery and K. Raynor, 2009, where Penn Treebank data is synthesized with eye movement data to come to new understandings of reading strategies). By and by, the dazzling efficiency and capabilities of contemporary corporate algorithms will be welcomed and celebrated even in the most humanistic disciplines. Let’s just hope that the authors of all these algorithms, corporate and humanistic, assume a rhetorical bent that respects the volition of their audience.