Abstract
Data visualizations are inherently rhetorical, and therefore bias-laden visual
artifacts that contain both explicit and implicit arguments. The implicit
arguments depicted in data visualizations are the net result of many seemingly
minor decisions about data and design from inception of a research project
through to final publication of the visualization. Data workflow, selected
visualization formats, and individual design decisions made within those formats
all frame and direct the possible range of interpretation, and the potential for
harm of any data visualization. Considering this, it is imperative that we take
an ethical approach to the creation and use of data visualizations. Therefore,
we have suggested an ethical data visualization workflow with the dual aim of
minimizing harm to the subjects of our study and the audiences viewing our
visualization, while also maximizing the explanatory capacity and effectiveness
of the visualization itself. To explain this ethical data visualization
workflow, we examine two recent digital mapping projects, Racial Terror
Lynchings and Map of White Supremacy Mob Violence.
Introduction
[1]
In March 2016, Microsoft’s Technology and Research division released an
artificial intelligence chatbot known as TayTweets under the handle @TayandYou
on Twitter. Microsoft had programmed the chatbot to interact with the online
community and subsequently learn from the tweets of users mentioning it, in
order to improve its natural language communication abilities. Though unlikely
to pass the Turing Test any time soon, TayTweets was nevertheless able to
generate its own internet memes, crack its own jokes, and participate in the
regular back-and-forth of online conversation.
Microsoft presented TayTweets as a value-neutral project that would showcase the
technological achievements of its research division, and early on the AI was
seemingly innocuous, posting anodyne responses to tweets welcoming it to Twitter
and asking it mundane questions. With its first tweet of “hellooooooo
w¿¿¿¿rld!!!” — a riff on the traditional output of a computer
programmer’s first program — TayTweets had virtually stepped onto the world
stage and greeted the Twitterverse in a seemingly innocent, even youthfully
naive, way.
Within 16 hours, however, the experiment had to be shut down, as TayTweets had
algorithmically learned to be racist. Posting inflammatory tweets championing
white supremacy and denigrating racial minorities and marginalized groups, the
artificial intelligence program had become an online menace and in fact a
cyberbully. TayTweets’s rapid descent into racist demagoguery serves as a
harrowing reminder that our digital productions are not free from the cultural
assumptions and prejudices that shape everyday human experience. Microsoft’s
designers had a blind spot about the depth and breadth of American racism, which
allowed TayTweets to replicate deep-seated preconceptions and prejudices without
critically examining them.
For our purposes, what is interesting about TayTweets is not its experimentation
with artificial intelligence, but the project’s apparent assumption that an
algorithm — the set of rules by which any programmatic approach operates,
whether digital or analog — is value neutral and divorceable from human
prejudice and malice. On the contrary, humans are at the center of algorithms,
not only as their creators but, in the case of data-driven algorithms, as the
producers of the content they shape and present. Though an extreme example,
TayTweets clearly demonstrates how wider cultural assumptions, prevalent
political ideologies, and public discourses shape the output of our algorithmic
productions, potentially replicating what we already know instead of aiding us
to discover the new and uncover the unexamined. In other words, TayTweets tapped
into an American zeitgeist riddled with detrimental preconceptions with regard
to race.
Ethical visualization is essentially a human-centric approach to algorithmic
production, considering the underlying biases, ideologies, and beliefs that
animate algorithms as they structure and reproduce past inequities and harmful
realities. All data visualizations — whether static or interactive, printed or
digital, computer-generated or hand-drawn — are algorithmic by nature, in
the sense that they solve complex problems of representation and require a set
of tree-based actions and decisions for their success. Ethical visualization
practices sit at the intersection of humanistic inquiry, ethics, and
communication design. We define ethical visualization as the presentation of
visualized information in ways that acknowledge and mitigate the potential for
harm engendered within the visualization form and content. While good design
practice forms the backbone of ethical visualizations, ethical visualization
practice goes one step further to consider the ultimate societal impact of such
design choices: do such choices cause harm or mislead, either intentionally or
unintentionally? Do they result in a net societal benefit, or do they prove
deleterious to marginalized individuals? These questions must be brought to the
forefront when considering good design, because a visualization can follow good
design practices and consequently be easy to understand, but still produce a
negative societal impact for its subject matter all the same.
Considering the object lesson of TayTweets, this article proposes that digital
humanists must adhere to a form of visualization ethics that considers how both
choices about working with data and the rhetorical qualities of communication
elements — color, composition, line, symbols, type, and interactivity — shape
users’ understandings of represented people and places. Given the “racism in the machine” — the ways
in which our digital tools can inadvertently recreate the latent racism,
underlying prejudices, and cultural blind spots in our society — the goal of
this visualization ethics should be “increasing understanding [for users] while minimizing harm” to
represented people and places [
Cairo 2014].
To propose a methodology for visualization ethics, this article examines two
projects that visualize the horrific history of racial lynching in the United
States, Lynching in America by the Equal Justice Initiative and Monroe Work
Today by Auut Studio, in order to show that no visualization is ideologically
neutral, but is instead part of an argument that must be critically examined. In
contrast to TayTweets’s outright bigotry, Lynching in America and Monroe Work
Today both present visualizations that demonstrate to varying degrees ethical
visualization practices, though one succeeds in producing an ethical
visualization to a greater degree than the other. This paper showcases ethical
visualization in practice to varying degrees in these two digital mapping
projects, demonstrating how choices about representation, interaction, and
annotation in their data visualizations either do harm in the sense described
above, or challenge dominant narratives. In comparing these two projects, the
article outlines a workflow that can ensure that data visualizations adhere to
best practices in visualization ethics and thereby present opportunities for
more inclusive and critical interaction with represented data.
Lynching in America (EJI) vs Monroe Work Today
Example 1: Racial Terror Lynchings Map
Lynching in America (
https://lynchinginamerica.eji.org/), a promotional website made
by Google for the Equal Justice Initiative, a mass incarceration
not-for-profit organization, contains an interactive map titled Racial
Terror Lynchings (
https://lynchinginamerica.eji.org/explore). Drawing on a dataset
compiled by the EJI for their eponymous report of “4075 racial terror lynchings of African-Americans,”
this interactive choropleth map of the United States purports to depict
“Reported lynchings by county”, occurring
between 1877 and 1950 [
"Lynching in America"]. Overall, the map has a
minimalist aesthetic, reminiscent of Google’s Material Design visual style
(
https://material.io/) that
depicts topographical elevation as well as country, state, and county
borders, but not cities, towns, roads, rivers, lakes, or landmarks (see
Figure 1). The data visualized is also
minimalist in presentation, and is tied directly to the state and county
borders depicted, as is typical of choropleth maps.
The map is focused on the southeastern United States. On first page load in
screen widths below 1500px, the map centers on the United States below the
Mason-Dixon line, while in larger screens the entire contiguous United
States is shown. However, in both cases, the American south grabs attention
with many southern counties highlighted. Users can click on any county or
state, and are taken to a zoomed-in view of the state, with the total
reported lynchings in that state displayed in large letters. The user can
then hover over individual counties to find out how many lynchings were
reported in that county (see
Figure 2). This
zoomed-in view focuses on the number of lynchings per state and county —
represented by polygons shaped according to county boundaries — creating a
visual argument that encourages measurement of states and counties as more
or less reprehensible in terms of number of lynchings. It is focused on
lynchings in the context of political boundaries, and thereby presents a
strong geopolitical argument about recorded lynchings that appears
definitive and damning, particularly of the states with the most counties
marked in bright red: Alabama, Florida, Louisiana, and Mississippi. There is
a straightforwardness about this visual argument: racial terror was and is
morally wrong, and its contours are plain to any competent observer.
In terms of color, the map features a dark, almost monochrome color palette,
with a dark grey United States segmented by black state lines, placed in a
dark blue sea. This mostly dark color scheme is dramatically interrupted
with many counties highlighted in various shades of red, with bright red
indicating 20 or more lynchings recorded in the county (see
Figure 1). The color scheme of red on an
otherwise dark, monochrome palette compounds the visual argument described
above, as it references the brutality and violence inflicted upon African
Americans in those locations, recalling the blood stains on United States
history as it relates to racial violence and white supremacy.
Example 2: Map of White Supremacy Mob Violence
The second example, Map of White Supremacy Mob Violence (
http://www.monroeworktoday.org/explore/), is a far more complex
visualization than the minimalist Racial Terror Lynchings. It is an
interactive map within Monroe Work Today (
http://www.monroeworktoday.org), a website dedicated to
publicizing the research of sociologist Monroe Work, who systematically
documented lynchings in the United States. Created by education-focused
digital agency Auut Studio, this interactive map of the United States
depicts lynching records in the context of historical racial violence and
public discourses of white superiority, and is consequently subtitled “The lynchings and riots to enforce racial superiority in
the US”. Upon first page load, Map of White Supremacy Mob
Violence stands in stark contrast to Racial Terror Lynchings, presenting the
entire contiguous United States center of screen, irrespective of screen
size, and representing each recorded case of lynching as a single grey dot
on a white national landmass background (see
Figure
3). State borders are not visible in this view, and a single,
bright, attention grabbing color is only found on instructions for users.
This initial map view — including map framing, choice of colors, and the
carefully worded title and subtitle — provides a strong and markedly
different message than the Racial Terror Lynchings map. Collectively, these
elements present a strong visual argument that discourses of white supremacy
are a nationwide reality in the United States, one that has historically
been enforced through lynchings and riots by mobs. The geopolitical framing
of lynching as a nation-wide reality is balanced with the acknowledgement of
a crucial aspect of the historically pervasive intellectual climate, one of
white superiority, and an important aspect of the social behavioral climate,
mob violence. At the center bottom of the page is a key annotation: a label
that reads “Should I trust this? Find out.” If users click this
message, they are taken to a plethora of information about the veracity of
the data used, and a discussion of the importance of thinking critically
about the visualized data.
Although it is not entirely clear from this initial page view what the grey
dots represent, the user is presented with instructions to zoom in and click
on individual points. After the user follows these instructions, the map
gains more color, and individual dots become more visible, along with state
and county boundaries (see
Figure 4). In this
view, state and county lines are visible, as are the boundaries of Native
American reservations and areas that historically were Spanish colonies.
However, these geographic lines are indicated with subtlety, using pale
colors. In contrast, the dots representing each individual lynching are
highlighted using bright colors and an
onclick
interaction effect. Marking geopolitical boundaries but deemphasizing them,
combined with emphasizing each individual lynching with bright colors, makes
the visual argument that lynchings occurred in the context of geopolitical
boundaries, but that the individual deaths are of greater significance than
the boundaries themselves.
Along the bottom of the screen, supplemental information and interactive
features appear, including a timeline, a label stating the time span
represented in the current view, and a color-coded legend of dot colors. Six
different dot colors are used: five to represent races of lynched people,
and one to represent “other”, where the race recorded in the historical
records does not fall into one of the main five. Notably, the “other”
category includes lynchings of white abolitionists, thereby demonstrating
the complexity of the history of lynching in the United States, and that
white people were also, however rarely, victims of lynching in the name of
white supremacy.
Both the timeline and the legend serve two purposes: one functional, the
other persuasive. Users can select a time period using the timeline, which
then alters data presented on the map, so they can see how many lynchings
occurred in an area over a specific timeframe. Secondly, the timeline
provides a persuasive visual cue that racial superiority-motivated lynchings
occured continually over a long timespan, with some time periods seeing
evidence of significantly more racial superiority-motivated lynchings. The
legend also works on these two levels. It firstly allows the user to
identify the race of a lynched person based on the color of the dot used to
represent them, and secondly provides a strong visual counterargument to the
widespread public assumption that lynchings were perpetrated exclusively on
African Americans. These two elements, the timeline and the legend, confront
the user with the temporal and racial extent of white superiority-motivated
lynchings, both qualities that are absent from the Racial Terror Lynchings
map.
Most strikingly, the zoomed-in view of Map of White Supremacy Mob Violence
contains a list, in the bottom left hand corner of the screen, of the name
(where available) of every lynched person represented by a dot in the
current map view, and the year they were lynched. Clicking on any individual
dot brings up a
callout box containing extra details
of the lynching available in the historical record, including the county in
which it occurred, the details of mob violence in which the lynching
occurred, the accusation made before the lynching, and links to every
available historical source that verifies the record (see
Figure 5). Naming individuals who were lynched,
and providing circumstances surrounding their death, focuses attention on
the humanity of victims of lynching and on the social circumstances in which
lynching was a viable possibility. In the present day context of many white
supremacists denying their racism, it is worth noting that these historical
records rarely mention race as a motivator for lynching. For example,
William B. Willis of Richmond County, Georgia, was accused of murder before
being lynched. The Map of White Supremacy Mob Violence does an exemplary job
of demonstrating that racism was indeed the motivating factor in lynchings,
and also that racism was largely hidden in the official historical record by
documenting other, non-racial reasons. Providing links to multiple
historical sources within the interactive map increases trust in the
veracity of data, while at the same time giving users the opportunity to
investigate the historical evidence themselves. Map of White Supremacy Mob
Violence uses multiple compelling strategies to both humanize the data it
represents, and to contextualize it in the societal racism and discourses of
white supremacy in the United States.
Comparison of both maps’ depictions of the West
The contrast between these two maps is even more striking when looking at the
west coast of the United States. For example,
Figure
6 shows a marked difference between the recorded lynchings in
California in the Racial Terror Lynchings map and the Map of White Supremacy
Mob Violence respectively. The difference in the representation can be
accounted for by the fact that the former only depicts lynchings of African
Americans, whereas the latter depicts lynchings of African Americans, Native
Americans, Latinos, Italians, and other races. The view of the American West
depicted in Map of White Supremacy Mob Violence in
Figure 6b provides a compelling visual narrative that lynchings
were common across California in the name of enforcing white superiority.
The view of Racial Terror Lynchings Map depicted in
Figure 6a, by its lack of clarity about exactly which data is
being represented (i.e. historical records of lynchings of African Americans
only), its use of a bold and expansive title that suggests comprehensive
coverage of lynchings of all races (i.e. Racial Terror Lynchings), and its
tonal emphasis on the south (California appearing grey, while visual
attention is drawn to the large amounts of red in the bottom right hand
corner of the map), makes a visual argument that California had few
instances of racial terror or lynchings. This is particularly problematic
because the Equal Justice Initiative’s stated goal is to challenge black
incarceration, and a key part of their organizational message is that this
goal is urgent and directly related to the history of lynchings motivated by
white supremacy. Black incarceration rates in California are among the
highest in the nation today: according to the U.S. Bureau of Justice,
California imprisons blacks 8.8 times more frequently than whites, well
above the national average of 5.5:1 [
"The Sentencing Project"]. Consequently
EJI’s visual argument in the Racial Terror Lynchings Maps inadvertently
breaks down in advocacy regarding California prisons.
Discussion
The emphases of these two maps are necessarily different because of the
different purposes of the sites in which they are situated. Lynching in
America is a promotional and advocacy tool for the Equal Justice Initiative,
primarily created to visualize data within (and thereby promote) the report
“Lynching in America”, which records
lynchings of African Americans and frames lynching as a societal tool —
enabled through mob violence and discourses of white superiority — to
subjugate African Americans between slavery and mass incarceration. It is in
the Equal Justice Initiative’s interests to visualize historical lynching
data in a way that draws attention to geopolitical divides, so that clear
links can be made between historical lynching events and present-day
constituencies of sitting politicians as well as county and state local
governments. The map provides compelling visual evidence for the
organization’s present-day advocacy work regarding the inequitable mass
incarceration of black Americans, the case of California notwithstanding.
The problematic aspect of this is that the Equal Justice Initiative’s
website, report, and Racial Terror Lynchings map are unfortunately named to
suggest that they cover all historical records of lynchings in the United
States. The Lynching in America report includes instructions for educators
who wish to use it as a teaching resource. In this context, the geopolitical
emphasis, use of color, use of summary data, and lack of links to sources
give a concerning impression that African-American lynchings were the
complete record of lynchings in the United States for the purposes of racial
terror.
The majority of both scholarship and public attention regarding lynchings
centers on the experience of African Americans in the Southern United
States, and for good measure: for blacks in the Jim Crow South, lynchings
represented a terrifying aspect of everyday life. In the grand scheme of
racial violence, however, lynchings represented one small piece in a complex
puzzle of individual, institutional, and structural racism. The conflation
of lynching with the full extent of racialized violence in United States
history obscures the historic depth and breadth of the oppression of people
of color. A black individual was far more likely to suffer public
humiliation, assault, rape, and murder than a public lynching. While
lynchings do not represent the totality of racial violence in America, they
come to the fore because they were highly symbolic affairs: gruesome
spectacles of white supremacy, racial violence, and bodily mutilation meant
to suppress and intimidate as much as they were meant to kill [
Wood 2009, 1–4]. Due to their highly symbolic nature and
the lasting implications of racist attitudes, policies, and actions for
African Americans today, lynchings have become synonymous with racial hatred
in the postbellum American south.
However, mob murder historically extended well beyond Dixie, representing a
form of prejudicial frontier “justice” in the Midwest,
West, and Southwest against minorities and immigrants of various backgrounds
[
Pfeifer 2006]
[
Pfeifer 2013]. Outside of the latest scholarship, such
victims, whom Carrigan and Webb describe as the “forgotten dead,” are largely overshadowed
or overlooked in the public sphere, missing an opportunity to explore the
structural, cross-regional, and transethnic dimensions of American lynching.
In fact, lynch mobs murdered hundreds of Mexicans between 1848 and 1928 in
the American Southwest [
Carrigan and Webb 2017].
Moving beyond EJI’s limited focus on African-American populations in the
American South, Auut Studio acknowledges the historic violence committed
against Native American populations, which is noticeably absent from the
vast majority of lynching data sets. This general oversight plays into
present-day blind spots regarding violence against native populations, who,
despite suffering more state violence and community disruption than any
other minority group in 2016 according to data collected by the Centers for
Disease Control and Prevention, rarely garner the public spotlight [
"The Counted"]. To counteract the public oversight of this “forgotten minority,” Auut
Studio included the boundaries of Native American reservations as “sovereignt[ies] deserving of
equal visual treatment on the map”
[
Ramey 2017]. Similarly, Auut Studio included lynchings of the Chinese along the
frontier, namely in California, as well as Mexicans in the Southwest,
keeping in line with recent historical scholarship.
In order to ethically represent historical subjects, data visualization
techniques, particularly those geared toward public consumption, must remain
abreast of the insights made in two areas of the scholarly literature:
debates on the subjects they depict, and debates on design and
representational considerations in the ethical visualization literature.
Present-day academic debates on lynching challenge the widely accepted
notion that lynching was exclusively a Southern phenomenon, excusing those
regions of the United States outside of the South of their own racist
heritage. The small but growing ethical visualization literature emphasizes
the need for acknowledging and mitigating the potential for harm inherent in
visualizing data, particularly when it comes to selection of design
elements, visual style, and selections of data to annotate and visually
emphasize [
Cairo 2014]
[
Hepworth 2016]
[
Kostelnick 2016]
[
Skau, Harrison, and Kosara 2015].
Collectively, we tend to visualize old arguments, as visualization practices
have not kept pace with dominant arguments in the digital humanities
literature about the importance of critical practices. Visualization
practice in the digital humanities runs the risk of following a
functionalist methodological approach that assumes visualization to be an
impartial medium. This illusory functionalism has led others to charge that
data collection, processing, and visualization practices constitute mere
“janitorial work” in the service of
“real” humanities scholarship, ignoring the important
decisions made during such processes that critically shape historical
narratives. Construing digital humanities practice as a “support field” has led to
further accusations that the digital humanities simply show to us what we
already know, rather than challenging us to think critically about
historical topics in new, interesting, and socially responsible ways [
Allington et al. 2016].
Following Alan Liu’s charge that digital humanists have ignored cultural
criticism, which in turn has blocked “the digital humanities from becoming a full partner of the
humanities,” data visualization practitioners need to critically
engage with the ways in which digital tools can “communicate humanity” rather than relegating it
to the margins, or worse, obscuring the human stories essential to
understanding structural racism today [
Liu 2012].
Visualizing data that exclusively focuses on the African-American experience
in the Southern United States provides an important argument about the
nature of Jim Crow racism. However, purporting such data to be an inclusive
representation constitutes a harm in the sense that it perpetuates common
narratives of racial violence as a southern exception to an otherwise
inclusive nation. After publishing their interactive map, the Equal Justice
Initiative itself recognized this oversight, acknowledging the 300 lynchings
of African Americans outside of the American south, though notably leaving
aside other ethnicities like Native Americans, Mexicans, and the Chinese
that fell victim to much of Western and Southwestern mob violence [
"EJI Releases New Data" 2017]. This overlooks the depth of structural racism and
its support of white supremacy, thereby denying the experience of millions
of present-day Americans. Historical information has the capacity to
legitimize or delegitimize present-day experience, and visualizations of
historical data are a particularly compelling and resonant medium through
which such information can either harm or help.
Ethical Visualization Workflow
We call for critical and practical analysis of the entire endeavor of data
collection and visualization in the digital humanities. Humanities scholars in
recent decades have critically examined the categories of scientific analysis
inherited from the enlightenment that presuppose essential differences (based on
sex, race, age etc) acknowledging that such pre-suppositions frame and
ultimately determine scholarly insight [
Knorr-Cetina 1981].
However, digital humanists and data scientists rely heavily on these categories
in their visualization practices precisely because they animate the entirety of
the scientific endeavor.
Similarly, visual communication has been studied for decades in terms of its
highly rhetorical qualities [
Barton and Barton 1985]
[
Gallagher et al. 2011]
[
Tapia and Hodgkinson 2003]. Despite early interventions by journalist Darrell
Huff and statistician Howard Wainer, data visualization literature and practice
seldom focus on the argument-altering, persuasive qualities of individual design
decisions or visualization conventions to a degree that allows for effectively
mitigating the harmful potential consequences of visualization [
Huff 1954]
[
Wainer 1984]. One notable exception to this overall trend is the
work of cartography scholar Mark Monmonier, who has long advocated for
acknowledging the complexity and nuance inherent in the minutiae of
visualization design decisions [
Monmonier 1991]
[
Monmonier 1995]. Huff, Monmonier, and Wainer can be seen as the
grandfathers of a small, interdisciplinary body of work on ethical visualization
practices that directly tackles the challenge of mitigating the potential for
harm inherent in data visualization [
Cairo 2014]
[
Hepworth 2016]
[
Kostelnick 2007]. We argue that there is an urgent need for this
ethical visualization literature to grow in detail and scope, particularly with
regard to digital humanities projects. It is imperative that ethical data
visualizers evaluate not only the rhetorical decisions of the analysis, but also
critically examine the entire process of working with data from collection to
final visualization and publishing.
Visual theorist Johanna Drucker offers one proposal to address the challenge of
ethical representation. She argues that the humanities need their own forms of
visualization, distinct from those developed for administrative and scientific
purposes [
Drucker 2011, 1]. She does this for good reason:
the standard visualization conventions that we are most familiar with — bar
charts, line charts, pie charts — were all created in European countries at the
height of their colonial expansion and industrial transformation. They were
created to track demographics, trade, war, and debt; all the trappings of their
growing empires [
Wainer 2013]
[
Cole 2000]. These visualization conventions carry this history,
and these associations, with them.
However, in his work on the role of charts in the social sciences, historian
Howard S. Becker reminds us that “if we invent a new format every
time we have something to say, we risk alienating users”
[
Becker 2007, 169]. Finding the right balance between visualization innovation and working
within established conventions is a complex procedure that demands a combination
of high visual literacy, advanced visualization production skills, intimate
understanding of the visualization context, and a critical perspective on the
entire data collection and visualization process. Much valuable work has been
done by geographers in terms of working critically with established
visualization formats, in the form of critical GIS [
Harvey et al. 2005]
[
Thatcher 2016].
We argue that for pragmatic reasons, humanists must work with the visualization
formats that are familiar to their audiences much of the time. We encourage
innovation in visualization practices only insofar as innovations are both
intelligible to the intended audience, and that they foster consideration of the
dignity of the represented subjects. Therefore, we propose an ethical
visualization workflow (see
Figure 7) that
operates within existing data collection and information design frameworks but
ensures that any given visualization’s argument provides a compelling yet
ethical and accurate representation of historical subjects.
Prior to creating a data visualization, a scholar following our ethical
visualization workflow would complete several critical steps: defining,
reviewing, collecting, pruning, describing, surveying, and pre-visualizing.
These steps involve processes that many digital humanities scholars will be
familiar with, with the important difference that they are suggested here with
alterations that we believe will result in an ethical data visualization. The
steps can be grouped into three standard digital humanities practice phases:
pre-data collection (defining, reviewing); data collection and curation
(collecting, pruning, describing); and data visualization and argumentation
(surveying, pre-visualizing, visualizing, publishing).
Pre-Data Collection
In the pre-data collection phase, the first step involves clearly defining
the subject area that the data visualization will depict, while the second
step involves reviewing the latest secondary literature on the topic at
hand. Reviewing subject area literature would inform the remaining steps in
the ethical data visualization workflow, inviting the researcher to
compensate for the data set’s shortcomings by seeking out and including new
information, or to limit the scope of the visual argument to be produced
with said data. Doing so would avoid the glaring oversights and interpretive
overreach that plagued the EJI’s Racial Terror Lynchings Map.
Data Collection and Curation
The second phase, data collection and curation, is perhaps most crucial in
producing an ethical data visualization, precisely because it is so
frequently overlooked. The third step in the ethical visualization workflow
involves collecting primary documents, artifacts, and datasets, as well as
secondary datasets of potential relevance, while the fourth step involves
checking the appropriateness, authenticity, veracity, and feasibility of use
of collected primary and secondary materials, and pruning those that don’t
hold up under scrutiny. Once these two critical data collection steps have
been finished, the researcher completes the fifth step, describing, by
creating their own dataset that combines the collected materials.
Creating a custom dataset for the researcher’s visualization in this way
eliminates other people’s and institutions’ biases from the data, ensuring
erroneous arguments are not unintentionally added through using unaltered
historical datasets. This process of collecting, pruning, and describing
data sets was undertaken by Auut Studios for ten years before visualization,
contributing to the particularly considerate treatment of ethical factors in
the Map of White Supremacy Mob Violence. Similarly, EJI created an extensive
dataset of over 4,000 public lynchings based on work done by Tuskegee
University and the research of E.M. Beck and Stewart E. Tolnay that
compellingly shows the long legacy of terroristic violence in the American
south. Nevertheless, decisions made about what counts or does not count as
“racial terror violence” made during the data collection phase —
namely to exclude the American frontier — ultimately shaped EJI’s visual
argument, making it overreach in its claims and thereby creating a narrative
around racial violence that excludes other minorities and other geographic
locales.
Data Visualization and Argumentation
The third and final phase of the visualization workflow, data visualization
and argumentation, is the main one associated with the ethics of data
visualization, and this phase involves four steps: surveying,
pre-visualizing, visualizing, and publishing.
Surveying & Pre-visualizing
Once the custom data set has been created, the researcher moves onto the
sixth step: surveying the latest literature from the small but growing
interdisciplinary field of ethical visualization. Surveying this
literature allows the researcher to keep abreast of ethical
visualization innovations and recommended best practices. The seventh
step, pre-visualizing, involves considering the contextual factors
around the visualization: normalizing representations of the data;
selecting the publishing medium; identifying intended and potential
unintended audiences based on that medium; and discerning between
visualization formats possible in that medium. When making decisions
about the argument produced by the visualization, it is imperative to
consider the larger contextual framing and the story that it tells. For
instance, in the case of EJI’s Racial Terror Lynchings, the data isn't
normalized against census population data. Normalizing the data in this
way would show how frequent these lynchings were in a population of
people rather than a geographic space, thus showing the relative
societal impact of a lynching of two people in a county of 100 rather
than 5 people in a county of 10,000.
In considering these contextual factors, the researcher can then select
the most appropriate visualization format, creating test visualizations
(these are more rudimentary than, and distinct from, alpha prototypes)
and performing any necessary re-structuring of the dataset based on
findings of this prototyping. In the pre-visualizing step, the selection
of visualization format is particularly important. The mechanics and
conventions of specific visualization formats contribute to determining
meaning. For example, Mercator geographic projections have been
criticized for privileging Northern Europe and underemphasizing the
Global South [
Monmonier 2010]. While they receive less
attention for their distortional effects, pie charts encourage a
comparison between visualized elements as if they make up a unified
whole, whether or not they actually do so in reality [
Tufte 1998]. It is not that such visualization formats are
by definition unethical to use, but that a critical perspective can
unearth the ways in which they limit or close off possibilities of
argumentation.
Critically examining each of the areas involved in pre-visualizing, and
making careful, considered choices on each area, can dramatically change
the ethical implications of a project. For example, if the Lynching in
America report and Racial Terror Map were printed documents, they would
have a much smaller audience than they have as web-based documents. This
would result in less reach for the organizations, but also less
potential harm in terms of the Racial Terror Lynching map being used in
contexts outside of lobbying for prison reform, and therefore giving an
erroneous presentation of the history of lynching in the United States.
The pre-visualizing step provides an opportunity to acknowledge the prior
understanding and cultural frame of the intended and unintended
potential audiences. The persuasive and culturally bound associations
those audiences necessarily have with design elements, explanatory text,
headers, legends and interaction experiences need to be considered. The
choice of colors and color ramps, as well as graphic or cartographic
elements like political boundaries, invariably influence the argument
produced by the visualization, as do map default views at certain screen
widths, and zoom options. To be ethical, these choices must be made with
the scholarly literature and the ethical visualization literature in
mind, as well as a critical perspective on the power of individual
design elements. To maintain visualization ethics, they should strive to
minimize harm while increasing understanding, and this can only be done
when the latest ethical developments in the field are factored into the
visualization at hand.
For instance, given the research on the dehumanization of deaths by
aggregating them into faceless statistics and the resulting inability to
enact meaningful social change [
Du Wors et al. 1960]
[
Bernard et al. 1971]
[
Slovic 2007]
[
Katz 2011], Monroe Work Today created an individual
marker for every person killed, so that their name and details could be
uncovered by the viewer. While more visually complex than a choropleth
map, as was used in Racial Terror Lynchings, creating individual markers
conveys the sheer gravity of the violence while not losing sight of the
individuals who suffered at the hands of white supremacy. As the
director of Auut Studio, RJ Ramey, explains,
it was [a] conscious
decision to include every person killed as their own marker on
the map — so that their name could be discovered. I have
received suggestions that a more “efficient”
visualization would have been a choropleth map or graduated
symbol size — and visualizers at EJI and the NYT have utilized
these methods. However, for purposes of respecting the gravity
of the violence and the humanity of the victims, I very much
believed it was most appropriate to provide the audience a
census of the lynching record, not a visual or numerical
analysis.
[Ramey 2017]
In contrast, EJI replicates the conventions, and thereby the limitations,
of how the Tuskegee Institute has visualized their data by state and
county since 1931 (see, for example:
Lynchings by
States and Counties, 1931,
https://www.loc.gov/resource/g3701e.ct002012).
The visual argument produced must be a bounded one, explaining what it
does show while inviting the user to interrogate and explore what it
does not show. Effacing the data, along with its assumptions, within the
visualization itself, as Map of White Supremacy Mob Violence so
admirably does, invites the user to judge the veracity and scope of the
data themselves, providing an opportunity for users to build informed
trust in the visualized data. Auut Studio makes plain the decisions made
during all of these pre-visualization steps, encouraging the user to
take a critical stance toward the argument ultimately put forth by the
visualization itself. On the other hand, while EJI does provide a link
to its full lynching report, which goes into detail about how the
dataset was collected and why it was bounded in such a way as to exclude
the American frontier, this link stands apart from the visualization
itself, buried within the website’s navigation drawer. Moreover, the
report does not address the translation of those choices into the
rhetorical qualities of the visualization itself, which purports to show
the entirety of American Racial Terror Lynchings. As the argument
presenting the data, an ethical visualization should provide clear and
apparent options for users to investigate sources, so that the user will
be able to get a critical sense of the underlying data set. Showing the
data effectively equates to showing your work for the user, but it does
not necessitate providing the user with what amount to false choices
that obfuscate the rhetorical value of the visualization in the first
place.
Visualizing and Publishing
The eighth step involves development of the visualization itself. To be
as ethical as possible, this needs to be an iterative process, beginning
with an alpha prototype, ending with a final visualization, and
including rounds of user testing with intended and unintended audiences
after each round of iteration. While user testing is not commonplace in
the digital humanities, it is a long-established practice in the allied
fields of computer science and visual communication design [
Nielsen 1993]
[
Sanders and Stappers 2012]. Such rigorous prototyping and testing
ensures the research mitigates harm to audiences that may result from
drawing associations and conclusions that are false, and that the
researcher could not predict. Lastly, the ninth step involves publishing
the visualization, the culmination of a thorough and considered ethical
research practice. Finally, in the interest of reproducibility and data
interrogation, it is vital to publish alongside the visualization its
underlying datasets or, in the case of lacking the requisite rights, to
provide ample documentation and citation of those datasets.
Feasibility of Proposed Workflow
Digital humanities teams can implement this workflow by centering their
activity on ethical questions around their subject area and the
technology used to present it. This can be accomplished using a twofold
approach: firstly, by familiarizing themselves with the latest research
in the content field and adjacent fields; secondly, by including team
members familiar with the entirety of the data pipeline from collection
to cleaning to presentation, as well as communication design principles
and user experience methods. User experience design is particularly
important for evaluating the interpretive intervention made by the
visualization and mitigating harm caused by the final visualization.
We recognize that the combination of skills we advocate is not the norm
in digital humanities projects and that it is rare for any one single
humanist to possess all of these skills. However, these are skills that
are common in several disciplines, particularly in the humanities
(source interrogation and intellectual framing); social sciences (data
collection, curation, and analysis); and communication design
departments (interface design and user experience). Interdisciplinary
collaboration in teams that contain — or consult with — humanists,
social scientists, and communication designers is the most feasible way
to implement the ethical visualization workflow. There is strong
interest in digital humanities collaborations in the field of
communication design, as evidenced by a recent special issue of Visible Language (see volume 49, issue 3), and
there are exemplary collaborations between social scientists and
humanists that can serve as models for such teams (see the cooperation
between the Digital Humanities and the Social Science D-Lab at
University of California, Berkeley).
Ethical data visualization is much more about priorities and planning in
digital projects than about increasing the amount of resources, or using
the latest technology. Whereas EJI’s Lynching in America had the full
support of Google Labs, Auut Studio’s Monroe Work Today resulted from
the careful consideration of a single individual working in consultation
with domain-area experts. Auut Studio began as a single-person
operation, but through proper planning, interdisciplinary collaboration,
and attention to historiographical implications, it was able to present
a more ethically minded visualization of lynching data sets.
Digital humanists need to bring their skepticism toward their source
material to bear on visualizations themselves, and consultation with
communication design faculty will illuminate the design and interaction
elements that need to be interrogated. The medium matters as much as the
content in shaping the message, and thus it is essential to understand
the medium in order to fully appreciate a visualization’s societal
intervention. In other words, as we stated at the outset of this paper,
the technology is not itself value-neutral.
Conclusion
Data visualizations are inherently rhetorical, and therefore bias-laden visual
artifacts that contain both explicit and implicit arguments. The implicit
arguments depicted in data visualizations are the net result of many seemingly
minor decisions about data and design from inception of a research project
through to final publication of the visualization. Data workflow, selected
visualization formats, and individual design decisions made within those formats
all frame and direct the possible range of interpretation, and the potential for
harm of any data visualization.
Considering this, it is imperative that we take an ethical approach to the
creation and use of data visualizations. Therefore, we have suggested an ethical
data visualization workflow — defining, reviewing, collecting, pruning,
describing, surveying, pre-visualizing, visualizing, and publishing — with the
dual aim of minimizing harm to the subjects of our study and the audiences
viewing our visualization, while also maximizing the explanatory capacity and
effectiveness of the visualization itself. To arrive at our ethical data
visualization workflow, we have examined two recent digital mapping projects,
Racial Terror Lynchings and Map of White Supremacy Mob Violence, to demonstrate
the potential pitfalls of data visualization, as well as suggest ethical ways to
avoid such pitfalls.
While EJI’s Racial Terror Lynchings is an admirable project, it nevertheless
presents an incomplete picture of racial lynchings in the American South in a
way that forecloses meaningful discussions about racism and white supremacy in
the American North and West, leaving out the “forgotten dead” among
frontier populations. By contrast, Monroe Work Today’s Map of White Supremacy
Mob Violence is an example of the possibilities of ethical visualization,
precisely because of the extended period in which the data were interrogated in
line with the latest scholarship in the field. Auut Studio brought a critical
eye to the topic at hand, acknowledging the shortcomings of the data and
investing the time to ultimately create a more inclusive picture of white
supremacist violence.
Monroe Work Today’s Map of White Supremacy Mob Violence is an exemplar of ethical
visualization, not because it is free of two centuries of baggage and biases,
but because it acknowledges them, while also acknowledging the potential
pitfalls of the very endeavor of transforming human beings into visualized
historical data. Unlike TayTweets, who consumed the racist prejudices and
ideologies of our modern society and became an intolerable digital bigot, Monroe
Work Today gives real thought to the quality and veracity of the data, the ways
in which that data represented (and did not fully stand in for) marginalized
persons, and the design decisions taken in visually representing that data. In
so doing, it presents an accessible, nuanced and compelling account of America’s
sordid history of lynching those on the margins. Likewise, by following our
process, readers can create similarly critical and self-effacing visualizations
that make apparent the argument, assumptions, and inherent flaws that animate
their digital humanities projects.
Works Cited
"Lynching in America" “Lynching in America: Confronting the Legacy of Racial Terror.”
Equal Justice Initiative. Equal Justice Initiative.
n.d., Web. 2 February 2018.
eji.org/reports/lynching-in-america.
"The Counted" “The
Counted: People Killed by the Police in the US.”
The Guardian, Guardian Media Group, n.d.
Allington et al. 2016 Allington, Daniel, et al.
“Neoliberal Tools (and Archives): A Political History of
Digital Humanities.”
Los Angeles Review of Books. 1 May 2016.
Barton and Barton 1985 Barton, B.F. and M.S.
Barton. 1985. “Toward a Rhetoric of Visuals for the Computer
Era”. Technical Writing Teacher, vol.
12, pp. 126–45.
Becker 2007 Becker, H.S. Telling About Society. University Of Chicago Press, 2007, pp.
167–185.
Bernard et al. 1971 Bernard, Viola W., et al.
“Dehumanization.”
Sanctions for Evil, edited by Nevitt Sanford and
Craig Comstock, Jossey-Bass, 1971.
Cairo 2014 Cairo, A. “Ethical
Infographics”. IRE Journal, vol. 37,
2014, pp. 25–27.
Carrigan and Webb 2017 Carrigan, W. D., and
Webb, C. Forgotten Dead: Mob violence against Mexicans in
the United States, 1848-1928. London: Oxford University Press,
2017.
Cole 2000 Cole, Joshua. The
Power of Large Numbers: Population, Politics, and Gender in
Nineteenth-Century France. Cornell University Press, 2000.
Drucker 2011 Drucker, J., 2011. “Humanities Approaches to Graphical Display”. Digital Humanities Quarterly, vol. 5, no. 1.
Du Wors et al. 1960 Du Wors, Richard E. et al.
“The ‘Mass Society’ and ‘Community’ Analyses
of the Social Present”. Conference on Statistics
1960, edited by E.F. Beach and J C. Weldon. University of Toronto
Press, 1962.
Gallagher et al. 2011 Gallagher, V.J. et al.
“Visual Wellbeing: Intersections of Rhetorical Theory
and Design”. Design Issues, vol. 27, no.
2, 2011, pp. 27–40.
Harvey et al. 2005 Harvey, F. et al. “Introduction: Critical GIS”. Cartographica, vol. 40, no. 4, 2005, pp. 1–4.
Hepworth 2016 Hepworth, Katherine. “Big Data Visualization: Promises and Pitfalls”
Communication Design Quarterly, vol. 4, no. 4,
2016, pp. 7-19.
Huff 1954 Huff, Darrell, How to
Lie with Statistics, New York: W. W. Norton, 1954.
Katz 2011 Katz, DL. “Facing the
Facelessness of Public Health: What's the Public Got to Do with It?”
American Journal of Health Promotion, vol. 25, no.
6, 2011, pp. 361-362.
Knorr-Cetina 1981 Knorr-Cetina, K.D. The Manufacture of Knowledge: An Essay on the Constructivist
and Contextual Nature of Science. Pergamon Press, 1981.
Kostelnick 2007 Kostelnick, C., “The Visual Rhetoric of Data Displays: The Conundrum of
Clarity”.
IEEE Transactions on Professional
Communication, vol. 50, no. 4, 2007, pp. 280–294.
https://doi.org/10.1109/TPC.2007.908725 Kostelnick 2016 Kostelnick, C. “The Re-Emergence of Emotional Appeals in Interactive Data
Visualization”. Technical Communication
63, 2016, pp. 116–135.
Liu 2012 Liu, Alan. “Where is
Cultural Criticism in the Digital Humanities?”
Debates in the Digital Humanities, edited by
Michael K. Gold. University of Minnesota Press, 2012.
Lynchings by States and Counties Lynchings by States and Counties in the United States,
1900-1931: (data from Research Department, Tuskegee Institute);
Cleartype County Outline Map of the United States. New York, NY: American Map
Company, 1931.
Monmonier 1991 Monmonier, M.S. How to Lie with Maps. University of Chicago Press:
1991.
Monmonier 1995 Monmonier, M.S. Drawing the Line: Tales of maps and cartocontroversy,
Henry Holt & Co, 1995.
Monmonier 2010 Monmonier, Mark. Rhumb Lines and Map Wars: A Social History of the Mercator
Projection. University of Chicago Press: 2010.
Pfeifer 2006 Pfeifer, M. J. Rough Justice: Lynching and American society, 1874-1947. University
of Illinois Press, 2006.
Pfeifer 2013 Pfeifer, M. J. Lynching Beyond Dixie: American Mob Violence Outside the South.
University of Illinois Press, 2013.
Ramey 2017 Ramey, RJ. 2017. Personal
correspondence. 13 November, 2017. Email.
Rushdy 2014 Rushdy, A. H. A. American Lynching. Yale University Press, 2014.
Sanders and Stappers 2012 Sanders, L., and P.J.
Stappers. Convivial Toolbox: Generative Research for the
Front End of Design. BIS Publishers, 2012.
Skau, Harrison, and Kosara 2015 Skau, D., L.
Harrison, and R. Kosara. “An Evaluation of the Impact of
Visual Embellishments in Bar Charts.”
Computer Graphics Forum. 34.3, 2015, pp.
221-230
Slovic 2007 Slovic, P. “‘If
I look at the mass I will never act’: Psychic numbing and
genocide.”
Judgment and Decision Making, vol. 2, 2007 pp.
79-95.
Tapia and Hodgkinson 2003 Tapia, A. and H.
Hodgkinson. “Graphic Design in the Digital Era: The Rhetoric
of Hypertext”. Design Issues 19, 2003,
pp. 5–24.
Tufte 1998 Tufte, Edward. The
Visual Display of Quantitative Information. Graphics Press,
1998.
Wainer 1984 Wainer, H.. “How
to Display Data Badly.” The American Statistician, 38(2), 1984, pp.
137- 147.
Wainer 2013 Wainer, H.. Graphic Discovery: A Trout in the Milk and Other Visual Adventures.
Princeton University Press, 2013.
Wood 2009 Wood, A. L. Lynching
and Spectacle: Witnessing racial violence in America, 1890-1940.
University of North Carolina Press, 2009.