#data feminism

[sanguinarysanguinity]

At the same time that these [redlining] maps were being made, the insurance industry, for example, was implementing similar data-driven methods for granting (or denying) policies to customers based on their demographics. Zoning laws that were explicitly based on race had already been declared unconstitutional; but within neighborhoods, so-called covenants were nearly as exclusionary and completely legal. This is a phenomenon that political philosopher Cedric Robinson famously termed racial capitalism, and it continues into the present in the form of algorithmically generated credit scores that are consistently biased and in the consolidation of “the 1 percent” through the tax code, to give only two examples of many. What’s more, the benefits of whiteness accrue: “Whiteness retains its value as a ‘consolation prize,’” civil rights scholar Cheryl Harris explains. “It does not mean that all whites will win, but simply that they will not lose.”

Catherine D'Ignazio and Lauren F. Klein, Data Feminism

Final quote is from Cheryl L. Harris, “Whiteness as Property,” Harvard Law Review 106, no. 8 (1993)

[exitwound]

The point here is that one does not need street names for navigation until one has strangers in the landscape. Likewise, data do not need cleaning until there are strangers in the dataset … once the data scientists involved in a project are not from within the community, once the place of analysis changes, once the scale of the project shifts, or once a single dataset needs to be combined with others—then we have strangers in the dataset.

Catherine D'Ignazio and Lauren Klein, Data Feminism

[librarycards]

Rather than valorizing the neutrality ideal and trying to expunge all human traces from a data product because of their bias, feminist philosophers have proposed a goal of more complete knowledge. Donna Haraway’s idea of the god trick comes from a larger argument about the importance of developing feminist objectivity. It’s not just data visualization but all forms of knowledge that are situated, she explains, meaning that they are produced by specific people in specific circumstances—cultural, historical, and geographic. Feminist objectivity is a tool that can account for the situated nature of knowledge and can bring together multiple—what she terms partial—perspectives. Sandra Harding, who developed her ideas alongside Haraway, proposes a concept of strong objectivity. This form of objectivity works toward more inclusive knowledge production by centering the perspectives—or standpoints—of groups that are otherwise excluded from knowledge-making processes. This has come to be known as standpoint theory. To supplement these ideas, Linda Alcoff has introduced the idea of positionality, a concept that emphasizes how individuals come to knowledge-making processes from multiple positions, each determined by culture and context. All of these ideas offer alternatives to the quest for a universal objectivity—which is, of course, an unattainable goal.

Catherine D’Ignazio and Lauren F. Klein, Data Feminism.

[emphasis added]

[sanguinarysanguinity]

The key to co-liberation is that it requires a commitment to and a belief in mutual benefit, from members of both dominant groups and minoritized groups; that’s the 'co' in the term. Too often, acts of data service performed by tech companies are framed as charity work (we discuss the limits of “data for good” in chapter 5). The frame of co-liberation equalizes this exchange as a form of relationship building and demographic healing. There is a famous saying credited to aboriginal activists in Queensland, Australia, from the 1970s: “If you have come here to help me, you are wasting your time. But if you have come because your liberation is bound up with mine, then let us work together.” What does this mean? As poet and community organizer Tawana Petty explains in relation to efforts around antiracism in the United States: “We need whites to firmly believe that their liberation, their humanity, is also dependent upon the destruction of racism and the dismantling of white supremacy.” The same goes for gender: men are often not prompted to think about how unequal gender relations seep into the institutions they dominate, resulting in harm for everyone.

Catherine D'Ignazio and Lauren F. Klein, Data Feminism

Footnote: Although the aboriginal activist quote ended up circulating on the internet as the work of one person—Lilla Watson—Watson herself describes it as the outcome of a collective process, and she desired that it be credited as “Aboriginal activists group, Queensland, 1970s.” See Watson, “Attributing Words,” Unnecessary Evils, November 3, 2008.

[sanguinarysanguinity]

Along similar lines, transgender activist and writer Dean Spade and computational biologist Rori Rohlfs, following Michel Foucault, theorize these [compounding effects of privilege] in terms of “life chances.” Sorting techniques like redlining distribute life chances differently for different populations, they explain. This “distribution of life chances” is key. Under the matrix of domination, life chances for majoritized bodies are enhanced, multiplied, and secured by new technologies, whereas life chances for minoritized bodies are diminished, divided, and imperiled by new technologies. Spade and Rohlfs, “Legal Equality, Gay Numbers and the (After?)Math of Eugenics.”

from a footnote in Catherine D'Ignazio and Lauren F. Klein, Data Feminism

[sanguinarysanguinity]

In other words, those who occupy positions of privilege in society are able to remain innocent of that privilege. Race becomes something that only people of color have. Gender becomes something that only women and nonbinary people have. Sexual orientation becomes something that all people except heterosexual people have. And so on. A personal anecdote might help illustrate this point. When we published the first draft of this book online, Catherine told a colleague about it. His earnestly enthusiastic response was, “Oh great! I’ll show it to my female graduate students!” To which Catherine rejoined, “You might want to show it to your other students, too.”

Catherine D'Ignazio and Lauren F. Klein, Data Feminism

[tjeromebaker]

La Inteligencia Artificial No Es Feminista

El mismo ChatGPT lo asegura: los sesgos en los conjuntos de datos utilizados para entrenar modelos de IA es posible que perpetúen estereotipos de género o discriminación.

El mismo ChatGPT lo asegura: los sesgos en los conjuntos de datos utilizados para entrenar modelos de IA es posible que perpetúen estereotipos de género o discriminación Fuente: Gaceta UNAM | Jun 17, 2024 La inteligencia artificial (IA) no tiene género, afirman sus creadores, sus impulsores, sus desarrolladores. Bien, en estos tiempos eso suena incluyente y progresista: la IA no tiene…

[sanguinarysanguinity]

omg this is the first time I’ve ever seen anyone (ง'̀-'́)ง with Edward Tufte.