This session falls under the theme 'Feminist Data Politics'. It aims to give participants a background and orientation to how 'big data' is not new, but emerges in the 19th century. In this session participants will explore the data collection by menstruation apps and think critically about what this means for a feminist politics of big data.

Begin session with giving some context to the history of quantification of bodies in 19th C Europe. Always good to customise this with a lot of local examples and contextually relevant material, for example, in the Asian GTI, we used examples from the colonial history of the region. Presentation: - Europe, 1800s-1900s – through industry, scientific advancement etc human knowledge and grasp of various anatomical realities – an idea of God was displaced. There was an explosion of ‘rationality’, ‘scientific’ truth... While mathematics is ancient, the way of cataloging and organising information is relatively modern that started in this period. Thus a growth of libraries, cataloging systems, indexing systems, field guides, and so on. - These advancements in discovery, invention, science - These times were also marked by slavery and colonial empires - All these ideas of development, change, transformation etc changed the European mindset – they came to think of themselves as superior. This is known as Social Darwinism. - The interaction between society and tech; it is not just a one-way thing,that tech creates an influence on society, but societies also shape, and re-shape technologies. - The emphasis on measurement, classification, numbering, naming, indexing led to the identification of norms, 'the normal', and outliers. This was not just about numbers, but was extended to people based on their behaviour. Criminals, prostitutes, the mentally unwell etc. these people were social ‘deviants’ and there was also an interest in carrying out scientific testing and experiments on these deviants to ‘understand’ them better. They were also considered as not valuable to the 'efficient' direction in which early modern European society was going. - One example of a sub-discipline developing within criminology was Bertillonage. Andre Bertillon started studying criminal’s bodies, physiognomy etc (size of head, size of ears, placement of ears on the side of the head, size of the lips etc) in prison to try and discern patterns between the physical make-up of criminals and the crimes they had committed, as well as compared to the general population. - Another example that connects these practices with colonialism was the story of Sarah Baartman: she was brought from South Africa as an exhibit to the paraded, measured (movie reference: Venus Noir). In Asia, while the above was documented more in the African context, the colonisers did census and gathered finger-prints to gather knowledge about the ‘natives’. - Photography developed as a medium, a volatile medium because it is so unstable, through which bodies were documented and identified. Present day examples of how race intersects with this unstable medium : the Apple watch, the Shirley Card for lighting in photography The session then moves on to the present day, looking at recent practices of quantification and big data. - The body becomes the best way to prove your identity, it becomes a very clear marker. Body scanning, facial recognition, fingerprinting, biometric databases etc. India has constructed the world's largest biometric database, called Aadhaar. - At the same time, the Quantified Self Movement sought to bring the control and power of quantification to the self; to quantify the self, to gather this knowledge as ‘self knowledge’. It started in 2007, founded by Kevin Kelly and Gary Wolff, were editors of Wired Magazine. - Fitbits and Unfitbits But who owns this data? How secure is this data? To creatively counter these narratives, there are two artist projects which challenge the notions of personal quantification. Session then moves on to an exercise: Exploring Menstruation Apps Ask participants to get into as many groups as there are apps to discuss; generally, it works well to have 4-5 people per group. Each group should be assigned one app from the list mentioned in the Arrow journal (see resources below) OR identify new/current/local apps based on your own knowledge of the landscape. Ask the group to spend 20 minutes finding out the following based on a review of the app, the privacy policy, and online searches - What the app does - What kinds of data it collects about the user - Where the data is stored - Who owns the data and what they do with it - Who made the app, and what are their motivations for it? Once the groups have completed the exercise, bring everyone back together for a feedback and review session.

- Presentation of history/background to what we mean by 'quantification' in the context of big data - Menstruation apps: Select from the list here: http://arrow.org.my/wp-content/uploads/2016/08/AFC22.1-2016.pdf pages 16-21 . We used Clue, Glow, and Kindara, - Develop a list of questions you want the group to explore (listed in the next section on methodology)