In W22, some of us read the book Data Feminism by Catherine D’Ignazio and Lauren F. Klein. We were able to listen to Lauren Klein in an LTC session and also ask her some more detailed questions in a session for the ones who read the book. Here some of my thoughts.

In our Q/A session, Klein reminded us that the purpose of the book comes out of frustration about the state of data and that it is geared towards advanced undergraduate students to introduce them to a number of different fields that are related to thinking about and working with data. The book is not aiming to be the authoritative voice but rather a space to open up the conversation even more. Note that for the online version, you can sign in and comment on the book. This kind of emphasis on collaboration and dialogue (with the intent that it is not just two sides but a multiplicity of perspectives) leads to better work, better products. In addition, the focus on situational and contextual factors again leads to better work and better products.

One statement that struck me as interesting because it also popped up in the LTC session on Climate Change in the Curriculum was the point that everyone knows that data sets are biased – but we don’t know what to do with this knowledge. Which is debilitating. (In the Climate Change session this was about that everyone knows about climate change but we don’t know what to do with this knowledge. Which is debilitating.). The book provides us with tools and strategies and core principles to get to through the sense of helplessness to a space where we can work with data in different ways. She also reminded us that, now that data has become demystified, it is no longer a privileged field – the people who believe to be in power are moving into AI, the new field of mystery.

In the actual LTC talk, Klein reminded us of the 7 core principles and gave us some of the examples out of the book that show how seemingly same data can lead to very different readings of the data, different visualizations – partly because the situation, the context are different. She also reminded us of the danger to assume that data is without emotion, that data visualization is rational and does not create emotion.

She also reminded us that not all data need to be collected, even though lots of data is not being collected out of lack of interest. Some data can be harmful to the people it represents, so we need to be careful in our use of data.