In an article published online last month by The Guardian—“AI programs exhibit racial and gender biases, research reveals”—the computer scientists behind the technology were careful to emphasize that this reflects not prejudice on the part of artificial intelligence, but AI’s learning of our own prejudices as encoded within language.
“Word embedding”, “already used in web search and machine translation, works by building up a mathematical representation of language, in which the meaning of a word is distilled into a series of numbers (known as a word vector) based on which other words most frequently appear alongside it. Perhaps surprisingly, this purely statistical approach appears to capture the rich cultural and social context of what a word means in the way that a dictionary definition would be incapable of.”
This tool’s ability to reproduce complex and nuanced word associations is probably not surprising to anyone familiar with digital humanities—and the fact that it returned associations that match pleasant words with whiteness and unpleasant ones with blackness, or that associate “woman” with the arts and interpretative disciplines and “man” with the STEM fields shouldn’t be surprising to anyone who has been paying attention. The distressing prospect that AI and other digital programs and platforms will only reinforce existing bias and inequality has certainly garnered the attention of scholars in media studies and DH, but one could argue that it has received equal attention in the social sciences.
As a graduate student in cultural anthropology drawn to DH, I sometimes find myself considering what exactly demarcates digital humanities from social science when apprehending these kinds of topics; somehow, with the addition of ‘digital’, the lines seem to have blurred. Both ultimately represent an investigation of how humans create meaning through or in relation to the digital universe, and the diverse methodologies at the disposal of each are increasingly overlapping. Below are just a few reasons, from my limited experience, as to why social scientists can benefit from involvement with digital humanities—and vice-versa.
1) Tools developed in DH can serve as methodologies in the social sciences.
Text mining, a process that derives patterns and trends from textual sources similar to the phenomenon described above, is particularly suited for social science analysis of primary sources. Programs like Voyant and Textalyser are free and easily available on the web, no downloads or installations required, and can pull data from PDFs, URLs, and Microsoft Word, plain text and more. Interview transcripts can also be analyzed using these programs, and the graphs and word clouds they create provide a unique way to “see” an argument, a theme, bias, etc.
Platforms like Omeka and Scalar can provide an opportunity not only to display ethnographic information for visual anthropologists, but can give powerful form to arguments in a way that textual forms cannot (see, for example, Performing Archive: Curtis + “the vanishing race”, which turns Edward S. Curtis’ famous photos of Native Americans on their heads by visualizing the categories instead of the categorized).
2) Both fields are tackling the same issues.
Miriam Posner writes that she “would like us to start understanding markers like gender and race not as givens but as constructions…I want us to stop acting as though the data models for identity are containers to be filled in order to produce meaning and recognize instead that these structures themselves constitute data.” Drucker and Svensson echo that creating data structures that expose inequality or incorporate diversity is not as straightforward as it seems, given that “the organization of the fields and tag sets already prescribes what can be included and how these inclusions are put into signifying relations with each other” (10). Anthropologist Sally Engle Merry, in The Seductions of Quantification, expounds on this idea in the realm of Human Rights, proving that indicators can obscure as much or more than they reveal. Alliances between DHers as builders and analyzers of digital tools and platforms, and social scientists as suppliers of information on the effects of these on the ground in various cultural contexts, provide benefit to both.
3) Emerging fields in the social sciences can learn a lot from established DH communities and scholarship.
Digital anthropology, digital sociology, cyberanthropology, digital ethnography, and virtual anthropology are all sub-disciplines emerging from the social sciences with foci and methods that often overlap with those of digital humanities. Studies of Second Life, World of Warcraft, or hacking; the ways diasporic communities use social media platforms to maintain relationships; or projects that focus on digitizing indigenous languages all have counterparts within digital humanities. Theoretically, there is much to compare: Richard Grusin’s work on mediation intersects with
anthropologists leading the “ontological turn” like Philippe Descola and Eduardo Viveiros de Castro; Florian Cramer’s work on the ‘post-digital’ pairs interestingly with Shannon Lee Dawdy’s concept of “clockpunk” anthropology, influenced by thinkers both disciplines share like Walter Benjamin and Bruno Latour.
Though I am still relatively new to DH, one theme I find repeated often, and which represents much of the promise and the excitement of digital humanities for me, is the push for collaboration and the breaking down of disciplinary boundaries. Technologies like AI remind us that we all share the collective responsibility to build digital worlds that don’t simply reflect the restrictions and biases of our textual and social worlds.
Kitty O’Riordan is a doctoral student in cultural anthropology at the University of Connecticut. Her research interests include anthropology of media and public discourse, comparative science studies, and contemporary indigenous issues in New England. You can reach her at caitlin.o’firstname.lastname@example.org.