My key takeaway from this week’s readings is summarized by Catherine D’Ignazio and Lauren Klein’s chapter title in Data Feminism “What Gets Counted Counts”. Public policy and decision making at regional to national to international level takes place in the modern-day world based on conclusions drawn from the data being collected. And what is in that data matters because that dictates what takeaways we have and how we better understand the world around us.

Being counted is the equivalent of having representation in the data age. When census surveys are done for example, allocation of resources and even the amount of representation a region gets is based on the results of the surveys conducted. This poses a problem in cities like New York City where there is a significant undocumented immigrant population, and the census fails to properly “count” them which leads to a lack of resources when compared to the actual population that such regions have.

In fact, during the last census drive, I had briefly volunteered with a community organization that was helping collect the survey forms and I have seen in person how many people in the community, particular undocumented immigrants and those seeking asylum do not want to fill up the survey in fears that it makes them vulnerable. This creates a problem because most of these people are already from a demographic that is underrepresented in politics and without being represented on the census, they are only going to be more underrepresented.

I also really liked the point that sometimes lack of data or missing data is representative of a situation itself. This is best described by the Guardian interactive “Does the New Congress Reflect You” where users can select their demographic and see how many people like them are in Congress. Clicking on “trans + nonbinary” leads to a blank map showing that there are no people in Congress like them. That is powerful as it shows the lack of representation for trans & nonbinary folks in the 2018 Congress.

Overall, what the chapter presents is the fact that data is not impartial and rather has biases and inequalities embedded in the whole process from collection, analysis and the conclusions drawn which in turn perpetuates the said biases and inequalities. Hence as data feminists, it is important to be able to consider the misrepresentations and injustices that are embedded in the data process to confront and tweak these issues.

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