Peter L. Forberg is a recent graduate of The University of Chicago’s undergraduate program in Sociology and graduate program in the Digital Studies of Language, Culture, and History. Drawing from qualitative social science methods and computational digital humanities methods, his work is broadly focused on mediated forms of social interaction with people, politics, and systems. In the field of DH specifically, he is interested in the codification of social theories into digital procedures. He intends to pursue a PhD in Sociology.
This is the source
the figurational approach criticizes the move towards social scientific essentialismthat to the following sentence:
Figurational sociology, developed by Norbert Elias, recognizes the risks of empiricism in representing complex human behaviors: In response, the figurational approach, “…blurs the procedural character of social phenomena by presupposing the existence of permanent structures subjacent to change” [Quintaneiro 2006, 55].
In this paper, I develop an online website that assists users in answering the
question, What do you need to remove in order to erase [thing] from [your life or
society]?
through network visualization methods. This project subverts the typical data
visualization strategy of working with extant data by encouraging users to produce data,
demonstrating how data visualization techniques can constitute a way of thinking and
creating rather than just analyzing and representing. However, this tool and its
affordances are primarily a case study for a sociological approach to critical design in
the Digital Humanities: By critically examining the tool itself (as well as the process
that created it), I am able to pose the broader questions of (1) how does the creation
of digital tools and projects reflect a theoretical interpretation of the social world
and its processes, and (2) how can these interpretations constitute the data of
sociological studies? In resolving these questions, I suggest that critical design can
be viewed as theory, experiment, and data: designs constitute a social theory, data can
be experimentally produced within this social theory, and encouraging reflective design
can turn the social theories of design themselves into data.
A look into website development which attempts to teach users about data creation and network visualization methods.
We shape our tools and thereafter they shape us.—John Culkin 1967, 70
In reflecting on the fraught disciplinary boundaries of the digital humanities, Lauren
Klein and Matthew Gold write, In what has been called ‘big tent’ DH, it can at times be
difficult to determine with any specificity what, precisely, digital humanities work
entails
[2016, intro.]mere use of digital tools for the purpose of humanistic research
or the study
of digital artifacts, new media, or contemporary culture
generative humanities
critical
digital humanities
purposes
and expands with the radical possibilities of productive practices and
processes. In their latest reflection on the discipline, Klein and Gold revise their
notion of DH as an expanded field
— a set of vectors of inquiry that are defined by
their tensions, alignments, and oppositions
— in order to argue for a DH that
matters,
one which incorporates socially oriented work
and translates the subtleties
of our research to others within the expanded field
vector of inquiry
that I engage with, positioning my project within the field by way
of useful but ultimately unsustainable distinctions.
This project is rooted in the principles of critical design;
that is, as a project in
which I am producing a digital tool, I am taking a critical, self-reflective approach to
my design process that interrogates the theoretical underpinnings of my design
decisions, an approach I am terming reflexive critical design
experimental critical design,
a mode of both sociological and DH research that
looks at the potential for interrogating digital tools as theories about the social
world. Experimental critical design recognizes the ability of digital tools to do more
than just analyze data but to produce data; moreover, it sees these digital tools (and
thus the theories underpinning them) to be data. After developing a framework for
critical design, firmly positioning this work within a specific vector of inquiry, I
will describe the digital project that I have made centered around network visualization
and then analyze it through the lens of this critical design framework.
In short, my digital project
big dataapproach to digital tools. DH can work towards an educative, public engagement to collaborate with a general population increasingly engaged with digital media. Together, the two critical design perspectives recognize critical design as theory, experiment, and data: designs constitute a social theory, data can be experimentally produced within this social theory, and encouraging reflective design can turn the social theories of design themselves into data.
David Berry and Anders Fagerjord argue for a critical reflexivity
scientization
of misinformation and hate
towards harmful political ends other
It is only with strict interrogation of digital tools and methodologies that DH can move
beyond producing instrumental
tools that say something about inputted data and instead
produce tools that say something about the world itself as it is being transformed and
interpreted by technology — tools that are, in their own right, theories about the
social world design
:
…design is a creative practice harnessing cultural, social, economic, and technological constraints in order to bring systems and objects into the world. Design in dialogue with research is simply a technique, but when used to pose and frame questions about knowledge, design becomes an intellectual method. In the hundred-plus years during which a self-conscious practice of design has existed, the field has successfully exploited technology for cultural production, either as useful design technologies in and of themselves, or by shaping the culture’s technological imaginary. As Digital Humanities both shapes and interprets this imaginary, its engagement with design as a method of thinking-through-practice is indispensable. Digital Humanities is a production-based endeavor in which theoretical issues get tested in the design of implementations, and implementations are loci of theoretical reflection and elaboration
In short, in the world of DH, design is a technological approach to representations of
the world which necessarily implements (and tacitly argues for) a theory about the
world. My work here is thus critical design,
a reflexive interrogation of the theories
underpinning the design I am attempting to implement in addition to the design itself as
it is used to argue for a particular representation of the world. This reflexive
critical design asks the designer to examine their own latent motivations, ideologies,
and biases; examine how these have been translated into the design; and examine design
itself as an act and as a principle. Reflexivity about research methods and approaches
is certainly nothing new in the humanities, especially as critical race theory and
gender theory have tasked readers with examining their implicit biases before engaging
with literary material methods
adds to this conversation by viewing approaches to research questions and data
— such as literary works or archaeological sites — as methods guided by a particular
point-of-view and sociological interpretation old ways
When working across academic registers, it’s important to establish a shared vocabulary:
in this instance, design
is a capacious term, as it can refer to both the design of a
study — from the social scientific lexicon — or the design of a computational tool. In
both cases, design is united by its generative design
as the term capturing the procedural logic of
computational tools which themselves constitute the research design. Procedural
arguments, or procedural rhetoric, consist of the art of persuasion through rule-based
representations and interactions, rather than the spoken word, writing, images, or
moving pictures
critical coding
perspective — what are the latent arguments built into software’s code
An example of how some social science researchers have critically studied their tools to
see their latent biases can help to elucidate the reflexive critical design perspective
I’ve outlined above: In the aforementioned facial recognition project by What work do
images do in AI systems? What are computers meant to recognize in an image and what is
misrecognized or even completely invisible?
In their case: what biases are baked into a
software’s procedures for recognizing faces? Their next step required choosing a dataset
to train their AI system, and, when critiquing one dataset representing human emotions,
they were aware of the arguments implicitly made by the dataset’s taxonomical
procedures, writing, First there’s the taxonomy itself: that ‘emotions’ is a valid set
of visual concepts. Then there’s a string of additional assumptions: that the concepts
within ‘emotions’ can be applied to photographs of people’s faces
see the outlines of a worldview
as it classifie[d] people
into a huge range of types including race, nationality, profession, economic status,
behaviour, character, and even morality
people
were, that these were
appropriate categories to use, and that a person’s categories were latent to their
physical appearance — what does it mean to look like a kleptomaniac
or a pervert
Reflexive critical design allows us to see the latent motivations, ideologies, and
biases inherent to the computational processes that constitute DH’s research tools.
These computational processes work in service of a worldview,
social theory, or, to
use the language of computer science, a model of the social world. The procedures that
classify images make an argument about essential human traits whereas natural language
processing procedures might make an argument about prescriptive grammar or semantics,
and GIS procedures might make an argument about national territories or land ownership.
In any case, without critical examination, these digital tools simply perpetuate the
worldview that constructed them, and so long as they produce data that people find
worthwhile, their latent biases can go uninterrogated.
Further, reflexive critical design allows for experimentation that creates data. For
example, in Crawford and Paglen’s project, the researchers could leverage the outputs of
their facial recognition software, as it was used by people online, to make an argument
about racism in digital technology. They made their digital tool accessible, allowing
users to submit their own photos and put the machine’s social theories to the test. In
taking a critical approach to design that gave them an intimate knowledge of their
software — and the inherent racial biases it modeled — they were better equipped to
critique its output. Crucially, reflexive critical design’s capacity for experimentation
negotiates between historical modes of criticism and emergent forms of critical making
A disciplinary anxiety emerges: the artistry and accessibility of Crawford and Paglen’s
work feels different than the social experimentation of Bail’s work — indeed, both are
framed differently — but at the crux of their digital methods is a recognition that they
are perpetuating a model of reality through procedural arguments that will result in
some form of data that puts their model to the test, allowing for a deeper reflection on
the software itself. These design procedures simultaneously function as research
procedures, with designers acting as researchers inviting users (or subjects) to be
subject to the digital design’s processes. Reflexive critical design is in direct
opposition to the work of the corporate sector, where digital processes are deployed
without reflecting on how those processes will affect human behavior, as the software’s
functionality and profitability are the primary motivators, forcing the software’s
underlying design logic to remain a trade secret
Finally, reflexive critical design itself constitutes data. There are a number of ways
of thinking about how design can be used as data: First, the design’s procedures
themselves, through a close-reading of the software’s code and processes emotions
dataset:
they drew out the social theories from
The final way that reflexive critical design can be seen as data is one that has rarely
seen use in DH or the social sciences broadly. Researchers can task people with
developing their own designs. Researchers can pose design challenges to participants in
an attempt to see how they might conceptualize and procedurally argue for a model of the
social world, and their procedural arguments constitute data to be analyzed. This sort
of thinking has gained popularity in the world of urban planning, where design
challenges
centered around tackling a central problem can be revealing of how
participants in the design challenge think through social issues
Critical, pedagogical, and generative approaches to game design bring the principles of
reflexive and experimental critical design into greater relief. As seen in Gilliam et
al.’s [2016] board game design workshops, games have long been theorized to have the
capacity to critique extant systems, teach new ways of thinking and acting, and produce
information, data, and action that matters,
as Karen Schrier
advocates for games that are not just
Many of those who advocate for games for social change place a great deal of emphasis on
those procedural arguments — fixating on how playing
on
an EMR system, these interfaces have procedures that make arguments about their
patients, medical hierarchies, and privacy — the push for health information exchange
(HIE) software which allows for the sharing of relevant data across health and
non-health entities such as food banks is reflective of a critical approach to health
care software design that implicitly argues for health systems which address social
determinants of health and gives patients more agency over their data
While design has always been focused on how best to analyze and represent data,
critical design brings latent social and political questions to the forefront of the
digital humanities, homing in on a DH that matters
Recent trends in employment, education, and technology have made the promise of large
scale data analysis a reality in a variety of industries and research areas, motivated
in part by the profit to be had from data mining (with the oft-repeated assertion that
data is the new oil), a budding career-oriented student body well-versed in technical
skills, and the proliferation of open-source software that has made mathematically
complex and technologically taxing data analysis accessible
Due to data science’s proliferation, open-access tools are more and more being
leveraged by actors who don’t have a deep knowledge of the technology’s operations, let
alone the statistical or social sciences background to put their results in context. In
a time that has been labelled the post-truth era
or
scienceor news, any additional credibility granted to amateurish data engineers is in itself a heightened risk
I chose to engage with network analysis tools because they are among some of the most vague and inscrutable data visualizations, often difficult to program and used for hyper-specific datasets. However, they are increasing in popularity, particularly with analyses of social media, so they are not entirely unfamiliar to a general audience. In the sections that follow, I will first describe the project’s digital technology, laying out what this tool is. Then, I will approach the tool from a reflexive critical design perspective, thinking through network theories, design principles, and design purposes to exhume the logic of this tool. Finally, I will engage with the concept of experimental critical design and demonstrate how this digital tool could be used to facilitate experimentation in a mode that is both sociologically-informed and germane to the digital humanities.
Nothing is something where something is meant to be.—Nick Cave and the Bad Seeds 2019
What do you need to remove in order to erase [thing] from [your life or society]?The website allows users to develop their own network visualization through a hierarchical visualization of HTML elements. By naming, arranging, and adding
nodesin the HTML document, users are able to seamlessly generate a visualization using the JavaScript library D3 (Data Driven Documents)
From a user experience standpoint, the design interface is rather simplistic. At this
point, it may be helpful to navigate to the website and access the page labelled tutorial,
as I’ll be walking through
the site’s functionality. The tutorial begins with an interactive statement seen in
Figure 1: Today, I want to remove a thing from society.
In the tool, the terms thing
and society
are actually dropdown menus which allow the user to produce different
statements, such as Today I want to remove a person from my life
or a concept from
history.
Based on the user’s selections, the program asks follow-up questions which lay
the foundation for their network. Thus, someone who decides to remove another person
from their life will be asked about what the two people share together, which will later
be turned into a node. Once the questions have been answered, the user is presented with
the full tool and a preliminary version of their network of loss. An example of what
this looks like is seen in Figure 2: On the left-hand side, there is a hierarchical
interface akin to an ontology or file management system where users can add nodes to the
network and give them names, sizes, and colors. This hierarchical structure can be
manipulated, with nodes rearranged or deleted. On the right-hand side, we see the
results of this hierarchy: when the “generate” button is pressed, the network is
constructed and visualized for the user as a series of connected nodes which can then be
dragged around. If the user wanted to save their network to be used in a different
visualization software, that option is made available to them with the export
button.
The example shown in the Figure 2 is taken from prototyping the tool with colleagues and
friends who eat meat. They were prompted with the statement, Today, I want to remove
meat from my life.
The networks developed by users in response included everything from
restaurants to holiday traditions. This use case resonated differently for different
people: for some the tool provided a visualization of just how much they would lose by
going vegetarian, while for others it enabled the construction of a thorough and
strategic plan for going meatless. In testing, people were asked to design networks that
touched upon deeply personal systems — such as removing a loved one from one’s life — or
systems that, while personally resonant, aren’t always easy to fully imagine — such as
removing the institution of policing from society. Even in these use cases, the tool
doesn’t make any assertion about whether loss is a good thing or a bad thing, nor does
it set parameters for what should or should not be included in the network. It simply
allows for these networks to be built and visualized. This is a decision that I made as
the administrator of these use cases: it would be entirely possible for the tool to be
used to ask the same questions, with different framing, in order to produce networks
that were critical of meat consumption or policing and posed questions to guide network
construction and interrogate people’s relationships to these systems. Such uses are
discussed more in the later section
While
…there is no amount of ‘big data’ that can be visualized to explain the very personal, individual experiences we have with systems and ideas. So instead of using data visualizations for rigorous science, we’ll leverage these tools to think through how we are implicated in systems.The aforementioned guided tutorial facilitates the
thinking throughthat the landing page requests. The tutorial is not an explanation of the tool itself but a line of questioning that is supposed to be evocative of different forms of thinking. These questions ask users to think more deeply about their engagement with systems and contingencies, and in this way, the website continues to make clear the designer’s motivations for its creation and the worldview it seeks to present. Should users skip the tutorial, the main tool still contains a rotating series of prompts that reiterate systems-level thinking, with pointed questions that provide pathways for expanding the network they’re creating to include things like historical structures or emotions. There is also a brief explanation of the tool’s features, but once again, users aren’t instructed to visualize certain topics or assume certain political positions. All of this is done to intentionally be content-neutral: the tool prepares them for
On the technical side, the program’s functionality is fairly straightforward: users are
essentially adding, deleting, and rearranging HTML elements using the hierarchy. These
div — or content division — elements are comprised of two main components: their
location and their customization. Each div — which we can understand as a node in the
network — is placed within an HTML hierarchy, wrapped within other divs. A child div is
located inside of a parent div. The child div knows the name of its parent based on its
location, and this HTML information is converted into network data — the links between
nodes — using JavaScript. The parent div knows how many children it has, which is only
used to make sure users do not accidentally delete the entire graph. Each div also has
customizable features — name, size, and color — that make it unique. These features are
also translated into network data using JavaScript and used to style the nodes in the
graph. It might be helpful to imagine the computational logic as akin to a giant
matryoshka doll: each interior doll knows which doll it is inside, and each doll has
unique features. Each div knows which div it is inside, and each div has unique
features. Because the network is generated on the basis of unique names for divs, users
can enter the same name multiple times to give a div multiple connections. D3, a data
analysis library for JavaScript that can turn JavaScript data into charts and graphs, is
used to convert the network data produced by the collaborating HTML and JavaScript in
order to visualize the nodes.inspect
feature — walks through how the code works, though
it expects some background knowledge in the basics of both HTML and JavaScript. For a
more detailed explanation of the website’s code, visit its public GitHub repository
here: https://github.com/peterforberg/loss
Before moving to the theoretical reasons for the project’s design, I want to explain why the tool’s UI and code are the way they are. First, the user interface was designed to be simplistic and approachable, with only a few variables (size, color, name, and position) that allowed for maximum creativity. In the design phase, there were other ideas for how the UI could look: for example, it was considered that the tool could begin with a monochrome screen, and as objects were removed (by being added to the network), the color, too, would be removed. This prototype would have more closely mirrored the actual process of loss, of things being taken away, but it wouldn’t have produced network visualizations that are familiar and easily introduced into other contexts. The design process included toying with other interfaces and features through an iterative engagement (initial designs were shared with colleagues and friends for feedback), but it ultimately settled on the one here for its ease of use, resemblance to other network visualization software, and technical simplicity — some features, like adding nodes via interaction with the generated network, were technically resource consumptive or difficult to use.
It should be noted that the code itself is not well-optimized, nor does it leverage advanced JavaScript libraries (aside from D3) to perform computations. There are a number of reasons for this: First, the goal was to program something with few dependences so that people with only an intermediate knowledge of the main web languages would be able to read its code. Second, as a fairly lightweight application, there was no need to optimize performance as ultimately, the code works. Finally, aspects of the code that are repetitive or mathematically simple better articulate the theory behind the code. For example, there are plenty of other ways to create a web app in which the hierarchy which generates the network leverages math to build out the network, but in this version, what happens computationally is what is envisioned theoretically: a parent node is known to be a parent because it quite literally contains its child node. They have a direct, computational relationship such that the computer “thinks” about the relationship the way we think about the relationship. Moreover, the computer “thinks” about the relationship the way network data represents those relationships, which is why the user can export the graph to a network data format. There’s a one-to-one relationship between computational representation, network theoretical representation, and the actual network relationships being represented. Thus, this software privileges simple, theoretically-driven computation to assist users in thinking through data, so the theory driving its design is what I’ll discuss next.
The data visualizations that led to the creation of this project are largely rooted in
the theories of network analysis. Network analysis has enjoyed widespread adoption in
the social sciences as a way of structurally examining the relationships between
different entities nodes
of the network are often straightforward — representing human
agents, organizations, or creatures — but the links between each of these nodes can
represent a variety of relationships. Some links are clearly defined: they might show
the transmission of diseases or check to see if two nodes are friends
on a social
media platform. Other network visualizations strategies actively delineate the changing
definitions of links and nodes in the visualization, such as in the project
companiesto concepts such as
geological processes.These two drastically different approaches to network visualization — one with very clearly defined sets of nodes and links, computationally generated, and one with ambiguous categories of nodes and links, qualitatively designed — demonstrate the potential for network visualizations to be used for projects that go beyond limited datasets and ask us more broadly to interpret the relationships presented in visualizations. Indeed, many visualizations fall somewhere in the middle, simply showing
relationshipsbetween nodes, a term which can take on a multiplicity of meanings.
While network theory and network analysis remain useful backdrops to network
visualization, they are intimately focused on
…blurs the procedural character of social phenomena by presupposing the existence of permanent structures subjacent to change
…on the understanding of the structures that mutually dependent human beings establish, and the transformations they suffer, both individually and in groups, due to the increase or reduction of their interdependencies and gradients of power
The language of figurational sociology is suited for network visualizations — often
referring to webs,
linkages,
dependencies,
and networks
— and a revival of
figurational sociology has come about in conversation with network analysis, which means
that there are visualizations that describe the relationships between actors in social
configurations encounters,
assemblages,
and precarious
encounters
— sometimes fleeting but still
important — between unexpected actors, assemblages
of perhaps apparently unrelated
systems and events, and the general precarity of the social world in a changing economy
and ecology. She, too, employs a constellatory logic that lets disparate subjects
converge to create something in their relationships to one another — in their
configurations.
The figurational approach, especially as it recognizes the strength of phenomenology,
tries to unpack these assemblages, encounters, and systems for what they are, revealing
their role in our daily lives and applying structure and systematic analysis where there
once was story, but Tsing’s work is evidence for how we tend to mask these analyses with
a deeply individual narrativization of complex systems. Reflexive critical design,
especially for a DH that matters,
tasks designers with self-reflection not just
because reflexivity helps explain the analytical framing of their work, but also because
it elucidates the political procedures of their work — as evidenced by the ways that
authors have demonstrated their desire to alter social behavior or critique racist
technology. Certainly, this project’s procedures are grounded in assisting users to
unpack their tendency towards individual narratives and think in terms of configurations
and systems, networks of related and contingent subjects who might sometimes be
invisible to individuals. The theoretical framing of figurational sociology is employed
procedurally, in the design of the website, to accomplish a goal that has political
implications. Imagine that this tool was employed not in its currently content-neutral
format but instead in a format that asked users to imagine a world without policing,
Amazon, or sexism: what would we need to remove to take down these institutions,
corporations, and prejudices? A simple alteration of the website’s language, with a
little more background provided on each subject, can quickly turn
The procedures of a digital tool, especially when used to educate and allow users to
produce more content, thus become an ethical and pedagogical practice. Historian Joan
Wallach Scott characterizes ethical practices as those in which people strive to reduce
the gap between reality as it is and reality as it ought to be
We shape our tools and thereafter they shape us
In the current design of the website, a small About
page invites users who have made
a network that they enjoy to submit it. One can imagine how a tool like this can be used
to produce data that can be recirculated and analyzed, recognizing the data for how it
was produced. If the tool was to be introduced in a classroom environment with a
specific prompt, such as What does it take to remove policing from society?
, then
students could think through the various ways that policing is implicated in their
lives, and each student’s developed network would represent a valuable data point in a
larger narrative about policing. Computationally, the tool is simple enough to edit that
one could port it to their own website and alter the tutorial such that the tutorial
prompts become a research question: one could code the prompt to state Today, I want to
remove social media from my life,
edit the follow-up questions, and distribute the
website to people as part of a research study on social media use. As experiment — and
perhaps as experimental pedagogy —
This pathway might seem circuitous — arriving at qualitative data analysis through the
techniques of quantitative, big data
approaches — and that’s because it is. Approaches
designed for large scale analysis can be applied to other settings, and the development
of these quantitative techniques can make qualitative analysis more robust. If a large
enough group of people use this tool to respond to the same prompt, then it is entirely
possible that that data could be collected and analyzed. Through data cleaning and
natural language processing techniques, the various individual networks could be
concatenated to make one larger network, and it would be possible to ask new questions:
what nodes appear across participants, what nodes receive the largest size, are nodes
consistently connected to the same central node across participants, and were most
participants thinking more about their own lives or about wider society? Of course, such
analysis needs perpetually to return to the data’s source and thus the theoretical
framework of the tool: it was a tool designed to produce data under a specific framing,
and so all data should be treated in relation to that framing. Here, a convergence of
research techniques seeks to break interdisciplinary boundaries: with qualitatively
generated data designed in such a way as to be able to be analyzed quantitatively, we
can further dismantle the distinction between these techniques and see the possibilities
for arguments that leverage multiple forms of analysis to demonstrate their claims.
Further, by anticipating big data
through a qualitative design, this skirts the
ethical dilemmas that accompany data-mining.
If data is to become more restricted to
protect privacy, then now is the time to start imagining alternative — perhaps more
traditionally experimental — ways to ethically source data. As an experiment,
Finally, the reflexive, critical interrogation of the design of
Second, this paper is in itself a datapoint in a larger conversation about network analysis and another conversation about the use of digital tools for ethical and pedagogical practices. Most network analysis tools are developed from purely mathematical perspectives, and theory is then applied to the results of that math by outside researchers who had no hand in shaping how the tool functions. This paper is different in that the data —
data,but in the under-utilized world of critical design, researchers are often looking for interlocutors whose work they can examine for its handling of biases and latent assumptions
Finally,
We are increasingly able to recognize systems for what they are, but they remain difficult to capture in their entirety. Moreover, each person experiences these systems differently. This is an open question: how would you think through loss and removal? How would you visualize it, communicate it to others?It then gives users the space to express their strategies for visualizing loss, though this information is not collected, merely used as a primer for the tool to come. Under the framework of experimental critical design, one could imagine asking participants to design an online tool that imagines loss — while I was building this tool, people who I shared my prototypes with had their own ideas for how one could go about visualizing and playing with the concept of removal. The divergent perspectives on how to procedurally represent loss are a final way in which critical design can constitute data; the process of design, opened up to many interlocutors, becomes a way of getting at people’s social theories of the world.
It’s important to reiterate that under the critical design as data
perspective, it is
the design itself that is data: the design of
designsto access theoretical dialogues — among designers or any group of people. This is inherently sociological: by using the design frameworks offered by digital humanities, researchers can obtain data about people’s understanding of the social world.
This paper has precariously moved across an assortment of disciplines, leveraging
theory, methods, and designs from anthropology, sociology, digital humanities, science
and technology studies, history, and data science to evaluate a tool designed with no
definitive disciplinary home. One could imagine going to the website and posing the
question, What would it take to remove critical design from academia?
The resulting
network would likely reveal the disciplinary anxieties that the turn to the digital has
allowed to foment. As digital tools become ubiquitous across fields of study, it is not
the job of the DH researcher or the sociologist or the computer scientist alone to
critically examine the tools that they use and produce (though they should), but it is
the job of the entire academic community to have a critical eye for the social
ramifications of technology that is found in classrooms, corporations, and people’s
personal devices.
While there are prominent examples of digital tools that are designed with a critical
reflexivity, standard practice in the digital humanities, as well as data science and
the social sciences, is to employ tools that go uninterrogated. Further, DH has stayed
closer to critique
than to design, typically employing tools designed for other fields
or general use. This has limited how DH is able to be productive: both how it is able to
produce theoretically-rigorous, content-specific tools and how it is able to produce its
own data for investigation.
Corporate tools face the scrutiny of the market to establish themselves, but academia
is not gifted with a talent for creating viral, widely-used digital platforms. Tools are
used for hyper-specific purposes, they become obsolete or unsupported, or they’re
designed without a strong enough team from different fields. For a DH that matters,
academia must recognize the necessity of interdisciplinary collaboration as opposed to
market competition, the collective power of designing socially-empowering and productive
digital tools. And academia shouldn’t forget its place in education: As the digital
divide in businesses and schools grow, DH can more concretely position itself as public
discipline engaged in digital pedagogy and committed to digital literacy that can
overcome the tendency of technology to further segregate people based on class, race,
and education
Design as a whole needs to rethink its approaches to data, ethics, and education. If digital tools are reshaping society, we need to be critical in our design of tools. If data is the new oil, then we shouldn’t become the new oil barons, chasing data at any cost. This is not to limit the work of researchers but to suggest that there are productive affordances of digital design that can open up new methods and strategies for analyzing and producing data while effecting social change.