Feminist Data in Practice: Sketching the Invisible
Talk + Panal Discussion under
Section Big Data and Algorithms of Intersectionality: Grounding Critical Queer-Feminist Research in the Digital Age
in 39th Congress of German Sociological Association
Big data has been widely suggested as the new symbol of neutral and objective knowledge. It is constantly argued to provide a full resolution to knowledge production. Compared to formal computational science, the possibilities of using current data-intensive approach like data mining enable the data-driven paradigm of science. However, critical voices emerged to question this kind of description and focus on the complexity and contradiction in the application of big data. Following the feminist theorists focus on science and technology studies such as Sandra Harding and Donna Haraway, I argue that data doesn’t necessarily speak for itself, but definitely speak for the people and the system who generated and presented it.
Because the way of reading, presenting, and communicating the existing data can make a significant difference, in this paper I will firstly analyze the concept of feminist data visualization that was brought out by data literacy researcher and artist Catherine D’Ignazio with its potential. Secondly, I will explore the approaches to collect and analyze data with an intersectional perspective from social informatics to feminist artificial intelligence, for example applying the insights of discussion for gendered artificial intelligence in the current context. From reading the existing data in the feminist way to doing data ourselves to tackle what Donna Haraway (1988) called “mutual and unequal structuring”, I will evaluate the process of encoding and decoding the narratives of big data. Thus except for seeing big data as disruptive challenge for the feminist epistemology, another way of seeing big data is by practicing it. This paper intends to illustrate how big data can serve as a medium to sketch the ignored, the oppressed and the invisible.