Not only do mapmakers get to choose what data to use (for example, the mapmaker of “American Migrations to 1880” claims he just took a “rough stab” at gathering data), but they can also change how the viewer interprets the data from how they present the information. For instance, by titling the “Invasion of America” map as an invasion instead of something such as “US Land Expansion,” the viewers of the map view the US’s actions in a negative light, whereas expansion would have seemed positive.

I wonder if one could reach a similar conclusion with Conrad that I did with Orwell: although he has an anti-imperialist outlook, he is wittingly or not deeply embedded in Orientalism and the broader idea of European supremacy.

It also means attending to the ways in which data science methods can inadvertently work to suppress those voices in the service of clarity, cleanliness, and control. Many of our received ideas about data science work against pluralistic meaning-making processes, and one goal of data feminism is to change that.

One field I would like to have as a part of the set would be some kind of background on the artist, as in place of origin, dates of birth and death, etc.

After looking at both objects and their descriptions, I think Washington and Lee did a much better job describing the Uncle Tom’s Cabin Vase than Davidson did describing the Georgia Club Photo. In Washington and Lee’s description, it is immediately apparent what the object is and why it is important to history. On the other hand, the Georgia Club Photo’s description only provides basic information about the object, and the viewer has to guess what the object actually is and the guess the significance of the object. Even though Davidson made the Georgia Club Photo text searchable, which was a nice touch, it is hard to even know what to search for if you do not clearly understand what the object is in the first place.

“Planning” may take a very loose form at first for an exploratory project based on a hunch, yet even a hunch has implications for what texts and what methods will be appropriate, and more explicit planning will eventually become necessary to avoid the wasted effort of a poorly conceived study. Conversely, any project may need to take new directions in response to the availability of texts and computational tools, and even well-defined and carefully planned projects often uncover promising and unexpected avenues of exploration and sometimes fail to produce significant results.

This means that we tend to want different tools than scientists, and also that we have some interesting data-wrangling problems. More often than not, the categories that our historical sources used to divide up our data are not the same ones we’re interested in analyzing.

Thus, by bringing this history to light through her archive, Zipf is able to provide a form of academic redress that attempts to subvert the matrix of domination through freedom of knowledge.

Because, of course, those archives are products of the time and place of their creation. The archives of the late 19th and early 20th century reflect what their creators felt was important to preserve. Moving into the 1950s, 60s, and 70s, archives and the archival profession changed, reflecting the political and social changes at work in society at large, as well as the emergence of social history—or history that studies the experience of ordinary people rather than just “great men.”

(1) Collect: Compiling counterdata—in the face of missing data or institutional neglect—offers a powerful starting point as we see in the example of the DGEI, or in María Salguero’s femicide maps discussed in chapter 1. (2) Analyze: Challenging power often requires demonstrating inequitable outcomes across groups, and new computational methods are being developed to audit opaque algorithms and hold institutions accountable. (3) Imagine: We cannot only focus on inequitable outcomes, because then we will never get to the root cause of injustice. In order to truly dismantle power, we have to imagine our end point not as “fairness,” but as co-liberation..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Yolanda Yang (4) Teach: The identities of data scientists matter, so how might we engage and empower newcomers to the field in order to shift the demographics and cultivate the next generation of data feminists?

In 2018, it was revealed that Amazon had been developing an algorithm to screen its first-round job applicants. But because the model had been trained on the resumes of prior applicants, who were predominantly male, it developed an even stronger preference for male applicants. It downgraded resumes with the word women and graduates of women’s colleges..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Julia Brous Ultimately, Amazon had to cancel the project.28 This example reinforces the work of Safiya Umoja Noble, whose book, Algorithms of Oppression, has shown how both gender and racial biases are encoded into some of the most pervasive data-driven systems—including Google search, which boasts over five billion unique web searches per day. Noble describes how, as recently as 2016, comparable searches for “three Black teenagers” and “three white teenagers” turned up wildly different representations of those teens. The former returned mugshots, while the latter returned wholesome stock photography.29

However, when many of us hear the term digital humanities today, we take the referent to be not the specific subfield that grew out of humanities computing but rather the changes that digital technologies are producing across the many fields of humanist inquiry.