Chapter two: “Collect, Analyse, Imagine, Teach” from Data Feminism, mainly discusses breaking down data can be a powerful strategy against oppression and biased information. The chapter contains three big ideas:
1. Data science creates biases such as racism and sexism when it’s in the hand of dominant groups:
The authors focus on data justice as they argue dominant corporates use data about social groups to make assumptions and decisions about individuals. More specifically, they use data science to gain personal information by tracking individuals and groups in order to limit their future potential. For example the “Redlining Map”, only secures the power of the makers (the white men in Detroit), and rather than securing the neighbor, it was a way of protecting their wealth as the information was only available to the white people only.
2. Not enough proof can cause a deficit narrative:
In the middle of the chapter, the authors talk about how using data as evidence or poof can cause an endless loop as it can be observed as not big enough or not trustworthy enough. For instance, when the media reports Black maternal mortality, they portray them as victims and fail to show how they work hard on the issues. Therefore, when collecting counter data as a piece of evidence it is important to be aware of how the subjects will be portrayed as.
3. Equity rather than equality, and co-liberation:
The chapter ends with how we should work forward in a society where everyone gets treated equitably, not equally, and it requires commitment and belief in co-liberation as we can design the flawed system rather than settling with injustice and we should teach our younger generations about data feminism by creating a learning experience that actually has relevance to its learners.
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There are some biased and untrue data-based facts about Mongolians on the internet. We are often stereotyped as people who have the best eyesight because we live in the countryside where we have to keep an eye on our horses and sheep, ride horses instead of cars, people are fluent in Russian and Chinese, and we live in the traditional yurt. In fact, because the datas are not updated for a long time and there aren’t many reliable sources that actually contain true information about us, all these assumptions, so-called “facts” are still being preserved and believed by many others because there are not many reliable sources that actually have the true information about us.