Archives & Counterdata

Biased and unpresentative datasets are one of the consequences incurring when people from dominant groups create most of our data products. Another effect could also be missing datasets. By exploring, collecting, systematizing, and analyzing counterdata - missing datasets that are not being collected because of e.g. bias, lack of social and political will, or structural oppression, we can find cultural indications of what is considered as important and what is not. This will not only allow us to see who benefits from – but also how bias and oppression might be baked into data practices and data products. Thus, collecting countadata can act as a way to explore the asymmetry between who is collecting, storing, and analyzing data, and whose data are collected, stored, and analyzed.