The module conceives of data as the products of human labour, of historically-specific circumstances of production that both structure presents and shape futures. Data are then never neutral. And yet the contexts that produce data are all too easily obscured by the presentation of data - and in particular digital data - as fact, and by the federation of data for access, into datasets, or as machine readable endpoints. Drawing on frameworks from intersectional feminism, anti-colonial theory, and infrastructure studies, this module takes a justice-led approach to analysing data, making data, and reusing data.
This will include:
- recovering the motivations behind apparently neutral classification systems (e.g. indices of disease or library classification systems)
- making a datasets and reflecting on the situated knowledge and decision making involved.
- training a machine to write structured data to establish the extent to which the future an algorithmic system can predict is limited by the data used to train that system.
No technical or theoretical knowledge is required to take this module. It is open to all, whether you want to develop a justice-led approach to thinking about the impacts of data in history, policy, or culture, or you want to work with data and want to apply justice-led thinking to your analytical toolkit.