What is Humanities Computing and What is Not?




John Unsworth


A talk delivered in the Distinguished Speakers Series

of the Maryland Institute for Technology in the Humanities

at the University of Maryland, College Park MD,

October 5, 2000



We are the mimics.  Clouds are pedagogues.

                                                                                Wallace Stevens, “Notes Toward a Supreme Fiction”



Any intelligent entity that wishes to reason about its world encounters an important, inescapable fact: reasoning is a process that goes on internally, while most things it wishes to reason about exist only externally.

                                                                Randall Davis et al., “What is a Knowledge Representation?”



I’ll give the short answer to the question “what is humanities computing” up front: it is foreshadowed by my two epigraphs.  Humanities computing is a practice of representation, a form of modeling or, as Wallace Stevens has it, mimicry.  It is also  (as Davis and his co-authors put it) a way of reasoning and a set of ontological commitments, and its representational practice is shaped by the need for efficient computation on the one hand, and for human communication on the other.  We’ll come back to these ideas, but before we do, let’s stop for a moment to consider why one would ask a question such as “What is humanities computing?” 


First, I think the question arises because it is important to distinguish a tool from the various uses that can be made of it, if for no other reason than to evaluate the effectiveness of the tool for different purposes: a hammer is very good nail-driver, not such a good screw-driver, a fairly effective weapon, and a lousy musical instrument.  Because the computer is—much more than the hammer—a general-purpose machine (in fact, a general-purpose modeling machine) it tends to blur distinctions among the different activities it enables.  Are we word-processing or doing email?  Are we doing research or shopping?  Are we entertaining ourselves or working?  It’s all data: isn’t it all just data processing?  Sure it is, and no it isn’t.  The goals, rhetoric, consequences, benefits, of the various things we do with computers are not the same, in spite of the hegemony of Windows and the Web.  All our activities may all look the same, and they may all take place in the same interface, the same “discourse universe” of icons, menus, and behaviors, but they’re not all equally valuable, they don’t all work on the same assumptions—they’re not, in fact, interchangeable.  To put a more narrowly academic focus on all this, I would hazard a guess that everyone in this audience uses a word-processor and email as basic tools of the profession (anyone care to admit that a secretary still types your manuscripts?).  But even though many of you are in the humanities, you do not all do humanities computing—nor should you, for heaven’s sake—any more than you should all be medievalists, or modernists, or linguists. 


So, one of the many things you can do with computers is something that I would call humanities computing, in which the computer is used as tool for modeling humanities data and our understanding of it, and that activity is entirely distinct from using the computer when it models the typewriter, or the telephone, or the phonograph, or any of the many other things it can be. 


The second reason one might ask the question “what is humanities computing” is in order to distinguish between exemplars of that humanities computing activity and charlatans (c.f. Tito Orlandi) or pretenders to it.  Charlatans—a strong word, I know—are, in Professor Orlandi’s view, people who present as “humanities computing” some body of work that is not: it may be computer-based (for example, it may be published on the Web), and it may present very engaging content, but if it doesn’t have a way to be wrong, if one can’t say whether it does or doesn’t work, whether it is or isn’t internally consistent and logically coherent, then it’s something other than humanities computing.   The problem with charlatanism is that it undersells the market by providing a quick-and-dirty simulacrum of something that, done right, is expensive, time-consuming, and difficult.  Put another way, charlatans trade intellectual self-consistency and internal logical coherence (in what probably ought to be a massive and complicated act of representation) for surface effects, immediate production, and canned conclusions.  When one does this, one is competing unfairly—in the market of deans, provosts, funding agencies, and private donors—with the project that is more thorough in its approach to the problem of representation and more thoughtful about what one might call economies of temporal scale—the long-term costs and benefits of painful planning vs. rush to production.  Now, the bad news is that all humanities computing projects today are involved in some degree of charlatanism, even the best of them.  But degree matters, and one way in which that degree can be measured is by the interactivity offered to users who wish to frame their own research questions.  If there is none offered, and no interactivity, then the project is pure charlatanism.  If it offers some (say, keyword searching), then it is a bit more real.  If it offers structured searching, a bit more real.  If it offers combinatorial queries, more real.  If it allows you to change parameters and values in order to produce new models, a bit more real.  If it lets you introduce new algorithms for calculating the outcomes of changed parameters and values, a bit more real, and so on.  This evaluative scale is not, as it seems to be, based on functional characteristics: it uses those functional characteristics as an index to the infrastructure—intellectual and technical—that is required to support certain kinds of functionality.  On this scale of relative charlatanism, no perfectly exemplary project exists, as far as I know.  But you see the principle implied by this scale—the more room a resource offers for the exercise of independent imagination and curiosity, the more substantially well thought-out, well-designed, and well-produced a resource it must be.  This is true regardless of the content—as true for E-commerce as it is for whatever we will call E-ducation, rEsearch, and the like. 


Finally, and most candidly, one asks the question “what is humanities computing” in order to justify, on the basis of the distinctions I have just drawn, new and continuing investments of personal, professional, institutional, and cultural resources.   This investment could take the form of a new grant-funded project, or a new undergraduate or graduate degree, or a new Center or Institute.  At this level, the activity that is humanities computing competes with other intellectual pursuits—history, literary study, religious study, etc.—for the hearts, minds, and purses of the university, even though, in practice, the particulars of humanities computing may well—and will likely—call upon and fall into traditional disciplinary areas of expertise.  So, as Willard McCarty has often noted, we have a problem distinguishing between computing in the service of a research agenda framed by the traditional parameters of the humanities, or, on the other hand, the much rarer, more peculiar case where the humanities research agenda itself is framed and formed by what we can do with computers. 


So, given that humanities computing isn’t general-purpose academic computing—isn’t word-processing, email, web-browsing—what is it, and how do you know when you’re doing it, or when you might need to learn how to do it? 


At the opening of this discussion, I said that “Humanities computing is a practice of representation, a form of modeling or . . . mimicry.  It is . . . a way of reasoning and a set of ontological commitments, and its representational practice is shaped by the need for efficient computation on the one hand, and for human communication on the other.”  I’ve long believed this, but the terms of these assertions are drawn from Davis, Shrobe, and Szolovits,  “What is a Knowledge Representation?” in a 1993 issue of AI Magazine.   As I unpack these terms, one at a time, I will begin by expanding my quotation of Davis et al. a little bit, on each point (you have these passages in your handout), and then look at some examples from the realm of humanities computing.


I. Humanities computing as model or mimicry: 

Davis et al. use the term “surrogate” instead of “mimicry” or “model.”  Here’s what they say about surrogates:

The first question about any surrogate is its intended identity: what is it a surrogate for? There must be some form of correspondence specified between the surrogate and its intended referent in the world; the correspondence is the semantics for the representation.  The second question is fidelity: how close is the surrogate to the real thing? What attributes of the original does it capture and make explicit, and which does it omit? Perfect fidelity is in general impossible, both in practice and in principle. It is impossible in principle because any thing other than the thing itself is necessarily different from the thing itself (in location if nothing else). Put the other way

around, the only completely accurate representation of an object is the object itself. All other representations are inaccurate; they inevitably contain simplifying assumptions and possibly artifacts.

Examples:              A catalogue record (vs. full-text representation)  [the catalogue record is obviously not the thing it refers to: it is, nonetheless, a certain kind of surrogate, and it captures and makes explicit certain attributes of the original object—title, author, publication date, number of pages, topical reference.  It obviously omits others—the full text of the book, for example.  Now, other types of surrogates would capture those features (a full-text transcription, for example) but would leave out still other aspects (illustrations, cover art, binding).  You can go on pushing that as far as you like, or until you come up with a surrogate that is only distinguished from the original by not occupying the same space, but the point is all of these surrogates along the way are “inaccurate; they inevitably contain simplifying assumptions and possibly artifacts”—meaning new features introduced by the process of creating the representation.  Humanities computing, as a practice of knowledge representation, grapples with this realization that its representations are surrogates in a very self-conscious way, more self-conscious, I would say, than we generally are in the humanities when we ‘represent’ the objects of our attention in essays, books, and lectures.


II. Humanities computing as a way of reasoning: 

Actually, what Davis et al. say is that any knowledge representation is a “fragmentary theory of intelligent reasoning,” and any knowledge representation begins with

. . . some insight indicating how people reason intelligently, or . . . some belief about what it means to reason intelligently at all. . . .  A representation's theory of intelligent reasoning is often implicit, but can be made more evident by examining its three components: (i) the representation's fundamental conception of intelligent inference; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends.  Where the sanctioned inferences indicate what can be inferred at all, the recommended inferences are concerned with what should be inferred. (Guidance is needed because the set of sanctioned inferences is typically far too large to be used indiscriminantly.) Where the ontology we examined earlier tells us how to see, the recommended inferences suggest how to reason.  These components can also be seen as the representation's answers to three corresponding fundamental questions: (i) What does it mean to reason intelligently? (ii) What can we infer from what we know? and (iii) What ought we to infer from what we know?  Answers to these questions are at the heart of a representation's spirit and mindset; knowing its position on these issues tells us a great deal about it.

Later on, the authors quote a foundational paper by Marvin Minsky, setting forth the frame theory.  Minsky explains:

Whenever one encounters a new situation (or makes a substantial change in one's viewpoint), he selects from memory a structure called a frame; a remembered framework to be adapted to fit reality by changing details as necessary.  A frame ... [represents] a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party.

And they go on to point out, in this quotation, how reasoning and representation are intertwined—how we think by way of representations.


Examples:              A Phone Book:  This is a very simple example, but it works. 

(i)                  the phone book's fundamental conception of intelligent inference? It assumes that you know someone’s name and will want to know their phone number, but more deeply, it assumes that name, address, and phone number are (for the purposes of the phone-book world-view) a complete person-record.

(ii)                 the set of inferences the representation sanctions?  It would support an analysis of name frequency, or knowing someone’s number and finding out their address, or other things, because all those relationships are in there, but

(iii)                the set of inferences it recommends?  It makes the name-to-number inference much easier than the others.

A Relational Database. Think about how a relational database establishes the grounds of rational inference by establishing fields in records in tables, and think about how it sanctions any sort of question having to do with any combination of the elements in its tables, but actually recommends certain kinds of queries by establishing relationships between elements of different tables.


III. Humanities computing as a set of ontological commitments:  

On this point, Davis et al. say:

selecting a representation means making a set of ontological commitments. The commitments are in effect a strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus, at the expense of blurring other parts.  These commitments and their focusing/blurring effect are not an incidental side effect of a representation choice; they are of the essence: a KR is a set of ontological commitments. It is  unavoidably so because of the inevitable imperfections of representations. It is usefully so because judicious selection of commitments provides the opportunity to focus attention on aspects of the world we believe to be relevant.

Examples:              OHCO (Renear, Mylonas, Durand: “Refining our Notion of What Text Really Is” from 1993—same year as the Davis article, though to be fair it draws on an earlier piece, DeRose, S. J., Durand, D. G., Mylonas, E., and Renear A. H. (1990), 'What is Text, Really?').  This view of text says that text is an ordered hierarchy of content objects, which means, for example, that content objects nest—paragraphs occur within chapters, chapters in volumes, and so on.  It also means that a language that captures ordered hierarchical relationships and allows content to be carried within its expression of those relationships can capture what matters about text.  Hence SGML.  But, as Jerry McGann and others have pointed out, this view of text misses certain textual ontologies—metaphor, for example—because they are not hierarchical, or more accurately, they violate hierarchy.  Davis et al. would say that’s not a sign of a flaw in SGML or in the OHCO thesis, but a sign that both are true knowledge representations—they bring certain things into focus and blur others, allowing us to pay particular attention to particular aspects of what’s out there. 


IV. Humanities computing as shaped by the need for efficient computation:  

Davis et al. explain,

From a purely mechanistic view, reasoning in machines (and somewhat more debatably, in people) is a computational process. Simply put, to use a representation we must compute with it. As a result, questions about computational efficiency are inevitably central to the notion of representation.

And later, they point out that different modes of representation have different efficiencies:

Traditional semantic nets facilitate bi-directional propagation by the simple expedient of

providing an appropriate set of links, while rule-based systems facilitate plausible inferences by supplying indices from goals to rules whose conclusion matches (for backward chaining) and from facts to rules whose premise matches (forward chaining).

Finally, they conclude that efficiency stands opposed in some way to the fullness of expression, and that

Either end of this spectrum seems problematic: we ignore computational considerations at our peril, but we can also be overly concerned with them, producing representations that are fast but inadequate for real use.

Examples:              OHCO again.   The reason SGML is the way it is, the reason it demands that elements nest properly within a specified hierarchy, is to enable efficient computation.  In fact, the SGML in its pure form was too flexible to really allow this, which is why certain features (like overlapping or concurrent hierarchies) were never implemented in software.  XML simplifies out of SGML some of the expressive possibilities that made SGML difficult to write software for, and viola, suddenly we have lots more software for XML than we ever had for SGML. 


V. Humanities computing as shaped by the need for human communication:

On this final point, Davis et al. say:

Knowledge representations are also the means by which we express things about the world, the medium of expression and communication in which we tell the machine (and perhaps one another) about the world. . . . a medium of expression and communication for use by us.  That in turn presents two important sets of questions. One set is familiar: How well does the representation function as a medium of expression? How general is it? How precise? Does it provide expressive adequacy? etc.  An important question less often discussed is, How well does it function as a medium of communication? That is, how easy is it for us to "talk" or think in that language? What kinds of things are easily said in the language and what kinds of things are so difficult as to be pragmatically impossible?  Note that the questions here are of the form "how easy is it?" rather than "can we?" This is a language we must use, so things that are possible in principle are useful but insufficient; the real question is one of pragmatic utility. If the representation makes things possible but not easy, then as real users we may never know whether we have misunderstood the representation and just do not know how to

use it, or it truly cannot express some things we would like to say. A representation is the language in which we communicate, hence we must be able to speak it without heroic effort.


Examples:              Well, SGML in general has raised in some the fear that humanists would never be able to speak it “without heroic effort.”  To be fair, good software removes some of the complexity—for example, by offering you only the elements that can legally be used in a particular point in the hierarchy.  But still, you have to be able to grasp the purpose and intent of the DTD in order to use it sensibly. 


VI.                Humanities Computing and Formal Expression:

There is also one feature of knowledge representations that Davis and his co-authors don’t mention, because their topic—“knowledge-representation technologies” (“the familiar set of basic representation tools like logic, rules, frames, semantic nets, etc.,”)—is “the primitive representational level at the foundation of KR languages.”  That feature is that knowledge representations are expressed in formal languages—languages “composed of primitive symbols acted on by  certain rules of formation (statements concerning the symbols, functions, and  sentences allowable in the system) and developed by inference from a set of axioms.  The system thus consists of any number of formulas built up through finite  combinations of the primitive symbols--combinations that are formed from the axioms in accordance with the stated rules.”  For our purposes, what is important about this fact is that it puts humanities computing, or rather the computing humanist, in the position of having to do two things that mostly we don’t do—provide unambiguous expressions of our ideas, and provide them according to stated rules.  In short, once we begin to express our understanding of, say, a literary text in a language such as SGML (standard generalized markup language), a formal grammar that requires us to state the rules according to which we will deploy that grammar in a text or texts, then we find that our ideas are subject to verification—for internal consistency, and especially for consistency with the rules we have stated.  This kind of error-checking (parsing) is new for humanities scholars (with the exception of philosophers of certain kinds). 


What does this do to us, what does it mean?


Questions and conversation?


References and Further Readings:


Davis, R.  H. Shrobe, and P. Szolovits.  “What is a Knowledge Representation?” AI Magazine, 14(1):17-33, 1993.  http://www.medg.lcs.mit.edu/ftp/psz/k-rep.html


DeRose, S. J., Durand, D. G., Mylonas, E., and Renear A. H. (1990), 'What is Text, Really?', Journal of Computing in Higher Education, 1.2: 3-26.


"Is Humanities Computing an Academic Discipline?" An Interdisciplinary Seminar at the University of Virginia (1999-2000): http://www.iath.virginia.edu/hcs/


McCarty, Willard and Kirschenbaum, Matthew. “Humanities computing units and institutional resources.” http://ilex.cc.kcl.ac.uk/wlm/hc/


McCarty, Willard,  “We would know how we know what we know: Responding to the computational transformation of the humanities.”  http://ilex.cc.kcl.ac.uk/wlm/essays/know/


Orlandi, Tito, “The Scholarly Environment of Humanities Computing: A Reaction to Willard McCarty's talk onThe computational transformation of the humanities.” http://RmCisadu.let.uniroma1.it/~orlandi/mccarty1.html


Renear, Allen, Elli Mylonas and David Durand.  “Refining our Notion of What Text Really Is: The

Problem of Overlapping Hierarchies”