As I mentioned in class, I just finished Perez’s Invisible Women: Data Bias in a World Designed for Men (2019). Perez cites some of the kinds of research we read for last week in her book.  She talks about how problematic many research methods are regarding everything from city planning to medical research. So many of these areas do not account for gender difference, and therefore they privilege male bodies and needs  as “typical” and women as “atypical.” In some cases the gender data gap leads to daily inconvenience for women, in other cases, it kills them.

The need to consider culture as part of this was also discussed. She tells of companies that made new “clean” stoves to help women in the developing world to avoid smoke inhalation caused by cooking over open fires. However, they failed to take into account the actual needs of these women in their design. They designed stoves that took longer to cook food, so many women reverted back to open fire cooking. In a report they then blamed lack of training of the women and not the stove design (and flawed data-gathering) as the problem that needed to be addressed so the women would start using their products.

This context, and the discussion last week, got me thinking about how data gathering often tries to sterilize the messiness of the world. Women can be hard to gather medical data on, for instance, because of hormonal fluctuations throughout the month. But not dealing with the messiness (like the academics in our case study were shown to always bring up) will continually leave someone out. While not all data can be collected perfectly, striving for better gender representation is surely a worthwhile goal. Women are half of the population, after all. In other words–let’s keep being academics and complicating things!

(Note: sorry I’m missing page number citations here. I listened to the audiobook and it has since been returned to the library!)