The Data Intensive Studies Center launches an art datathon and discusses feminist data science

On February 11, the Data Intensive Studies Center kicked off its 2022 Art Datathon with a “What Does Feminist Data Science Look Like?” conference by Catherine D’Ignazio, assistant professor of urban science and planning at MIT, director of the Data + Feminism Lab and co-author of the book “Data Feminism” (2020).

D’Ignazio discussed how feminist research can be used to create more just and ethical data practices, especially for marginalized communities.

“‘Data feminism’ [is] part of a growing body of work that seeks to hold corporate and government actors accountable for manufacturing sexist, racist and classist data products,” said d’Ignazio. “So if you’ve followed the space at all, it’s the space of algorithmic bias, fairness in artificial intelligence, ethics in machine learning.”

D’Ignazio explained that data science is not necessarily neutral. On the contrary, it can be discriminatory and prejudicial.

“It’s things like face detection systems that can’t see women of color, hiring algorithms that downgrade women’s resumes. [and] child abuse detection algorithms that punish poor parents and many more examples » said d’Ignazio.

In providing these examples of harmful technology practices, D’Ignazio argued that data feminism is powerful.

“The core argument we make in the book is that intersectional feminism, when applied to the unequal balance of power in data science, can help that power be challenged, rebalanced, and changed,” said d’Ignazio.

D’Ignazio noted that there are seven tenets of data feminism: examine power, challenge power, rethink binaries and hierarchies, elevate emotion and embodiment, embrace pluralism, consider context, and make visible work.

D’Ignazio illustrated the first principle, examining power, using “The Library of Missing Datasets” (2016), an artwork by Mimi Onuoha. The article documents the missing datasets that Onuoha has identified, said d’Ignazio.

“These are datasets that a reasonable person might expect to exist, but in fact, they are datasets that don’t – things like trans people killed or injured in crimes of hatred, people excluded from social housing due to criminal records, etc. at,” said d’Ignazio.

Over a period of years, Onuoha – whose background is in journalism – kept a running list for each time she encountered a missing data set. Onuoha inserted titled folders with the missing datasets into a white binder.

“You can browse folders. … Of course, when you open the folder, it’s empty,” said d’Ignazio. “The data is missing, there are no records in the dataset. And so the point she’s trying to make in this article is that these datasets are missing for a reason.

The absence of certain data sets can be attributed to societal power imbalances, D’Ignazio argued.

“This power imbalance is what determines what data is collected and what is not; what research is conducted, what research is not; and who has the resources to undertake these things and who does not,” said d’Ignazio. “So governments have that power, monetary institutions have that power, minority groups and communities generally don’t.”

D’Ignzaio explored the second principle of data feminism through the theme of femicide – gender-related killings of cis and trans women – in Mexico.

“[Femicide is] legally defined as crimes in a handful of countries, including Mexico, where this project was based,” said d’Ignazio. “However, even though there are laws in place, the state does not routinely collect data on femicide.”

In “Data feminism, the second chapter explores Maria Salguero, a researcher who has collected data on femicide in Mexico.

“[Salguero] has assembled the most comprehensive public record of femicide in the Mexican context,” said d’Ignazio. “She’s helped families find loved ones, she’s provided data to journalists and NGOs, and she’s even been called before the Mexican congress to testify on multiple occasions.”

Salguero’s role in collecting data when the Mexican government has failed to do so is what D’Ignazio sees as “counter-data,” a powerful way to use data to go against the law. unjust balance of power.

At the end of her talk, D’Ignazio answered questions from attendees, including one about how government agencies can trust feminist data to produce progress for communities.

“What is interesting with the counter-data produced by these activists, feminist collectives, civil society groups is that this data often becomes the most reliable source of information,” he added. D’Ignazio answered. “They end up being statistics cited by the UN, … or the United States Department of State … end up citing the work of these women-led data organizations.”

Anna Haensch, senior data scientist at the Tufts Data Intensive Studies Center, asked about the relationship between capitalism and data feminism.

“Capitalism is definitely at odds with data feminism,” D’Ignazio answered. “If by capitalism we mean an economic system that hoards resources for a very small number of people and externalizes the social and collective costs of operations to the public, then of course these fall disproportionately on the shoulders of people more vulnerable and marginalized. So this system, I think, is very incompatible with data feminism and with intersectional feminism more generally.

The 2022 Art Datathon continued with a “Finding Biases in Museum Collections” panel, which included Kelli Morgan, Diana Greenwald and Chad Topaz.

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