Analyze

West Africa

Misria
Annotation of
In response to

At the height of the West African Ebola epidemic, West African governments and Mobile Network Operators (MNOs) were barraged with requests from international humanitarian and Western data analytics agencies to provide Call Detail Record data. This data could furnish the large-scale ambitions of data modelling to track and predict contagion. Despite its utility in tracking mobility and, as such, disease, CDR’s use raises many privacy concerns. In addition, embedded within a turn towards datafication, CDR technologies for surveillance embed specific ontologies of the data-focused society they emerge from. There is a false equivalence embedded in the relationship between humans and technology. The predominantly Western idea that one phone equals one person underlines the claim that CDR data accurately tracks distinct user movements, encoding a Western “phone self-subjectivity” (Erikson 2018). However, the refusal by some African actors to hand over sensitive mobile data to international agencies was met with forceful rhetoric of Africa’s moral obligation to comply—to forgo privacy rights in the name of ‘safety.’ The Ebola context reflects an emergent digitization of emergencies in the Global South, which is reshaping the way societies understand and manage emergencies, risk, data, and technology. The big data frenzy has seen a rising demand to test novel methods of epidemic/pandemic surveillance, prediction, and containment in some of the most vulnerable communities. These communities lack the regulatory and infrastructural capacity to mitigate harmful ramifications. With this emergence is a pivot towards 'humanitarian innovation,' where technological advancements and corporate industry collaboration are foregrounded as means to enhance aid delivery. In many ways, these narratives of innovation and scale replicate the language of Silicon Valley’s start-up culture. Surveillance of the poor and disempowered is carried out under the guise and rhetoric of care. In this scenario, market ideals and data technologies (re)construe social good as dependent on the “imposition of certain unfreedoms” as the cost of protection (Magalhaes and Couldry 2021). As big data technologies, they foreground a convergence of market logistics and global networks with existing and already problematic international humanitarian infrastructures (Madianou 2019). These convergences create new power arrangements that further perpetuate an unequal and complex dependency of developing countries on foreign organizations and corporations. Pushback against these data demands showcases competing notions of where risk truly lies. While resistance to data demands was at the state level, community responses to imposed epidemic regulations ranged from non-compliance to riots. These resistances demonstrated how the questions of ‘who and what is a threat?’ or ‘who and what is risky?’ and ‘to whom?’ experience shifting definitions in relation to these technologies as global, national, and community imaginaries are reinforced and reproduced as cultural, political, as well as biological units. 

Source

Akinwumi, Adjua. 2023. "Technological care vs Fugitive care: Exploring Power, Risk, and Resistance in AI and Big Data During the Ebola Epidemic." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science.

West Africa

Misria
Annotation of

(MNOs) were barraged with requests from international humanitarian and Western data analytics agencies to provide Call Detail Record data. This data could furnish the large-scale ambitions of data modelling to track and predict contagion. Despite its utility in tracking mobility and, as such, disease, CDR’s use raises many privacy concerns. In addition, embedded within a turn towards datafication, CDR technologies for surveillance embed specific ontologies of the data-focused society they emerge from. There is a false equivalence embedded in the relationship between humans and technology. The predominantly Western idea that one phone equals one person underlines the claim that CDR data accurately tracks distinct user movements, encoding a Western “phone self-subjectivity” (Erikson 2018). However, the refusal by some African actors to hand over sensitive mobile data to international agencies was met with forceful rhetoric of Africa’s moral obligation to comply—to forgo privacy rights in the name of ‘safety.’ The Ebola context reflects an emergent digitization of emergencies in the Global South, which is reshaping the way societies understand and manage emergencies, risk, data, and technology. The big data frenzy has seen a rising demand to test novel methods of epidemic/pandemic surveillance, prediction, and containment in some of the most vulnerable communities. These communities lack the regulatory and infrastructural capacity to mitigate harmful ramifications. With this emergence is a pivot towards 'humanitarian innovation,' where technological advancements and corporate industry collaboration are foregrounded as means to enhance aid delivery. In many ways, these narratives of innovation and scale replicate the language of Silicon Valley’s start-up culture. Surveillance of the poor and disempowered is carried out under the guise and rhetoric of care. In this scenario, market ideals and data technologies (re)construe social good as dependent on the “imposition of certain unfreedoms” as the cost of protection (Magalhaes and Couldry 2021). As big data technologies, they foreground a convergence of market logistics and global networks with existing and already problematic international humanitarian infrastructures (Madianou 2019). These convergences create new power arrangements that further perpetuate an unequal and complex dependency of developing countries on foreign organizations and corporations. Pushback against these data demands showcases competing notions of where risk truly lies. While resistance to data demands was at the state level, community responses to imposed epidemic regulations ranged from non-compliance to riots. These resistances demonstrated how the questions of ‘who and what is a threat?’ or ‘who and what is risky?’ and ‘to whom?’ experience shifting definitions in relation to these technologies as global, national, and community imaginaries are reinforced and reproduced as cultural, political, as well as biological units. 

Akinwumi, Adjua. 2023. "Technological care vs Fugitive care: Exploring Power, Risk, and Resistance in AI and Big Data During the Ebola Epidemic." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science. Honolulu, Hawai'i, Nov 8-11.

What steps does a user need to take to produce analytically sharp or provocative data visualizations with this data resource?

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The UI for the portal is straightforward and easy to use and also doubles as a GIS. Through the advanced search function users can use either the criteria or filter tabs to narrow their searches to specific sites. For example when you narrow down the search to RMP facilities only you can quickly pinpoint all of these facilities on a map of an area to show how burdened an area may be with these types of facilities.

What data visualizations illustrate how this data set can be leveraged to characterize environmental injustice in different sett

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The data can very quickly show you how many facilities a geographical area may have. This can allow users to see how burdened a neighborhood for example may be with specific facilities.

What visualizations can be produced with this data resource and what can they be used to demonstrate?

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One of the only data visualizations this site offers is plotting down pinpoints on a map showing individual facilities. If there is more than one site in a certain geographical area then it will group the sites together and provide a circle for where the sites are contained with the number of sites listed on the circle. This makes this data resource not super flexible in ways it can display information. However this is a helpful visualization as it can quickly show you how many specific facilities a certain location may have

 

You can also generate simple graphs with the data that displays the amounts of certain facilities throughout the state. This is a good tool for tracking all regulated facilities which can help users address Ej on a statewide scale.

What can be demonstrated or interpreted with this data set?

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What this lacks in visualizations it makes up for drastically in easy to use UI and for creating one location for all of the state's facility data. By using its advanced search tool users can quickly find a plethora of data on extremely specific sites. This tool will show when the facilities had their most recent evaluations and whether or not there were violations, rough estimates on onsite stored chemicals, which regulatory programs they are a part of, CalEnviroScreen percentile ranges, and a contact list for facility employees.

How scales (county, regional, neighborhood, census tract) can be seen through this data resource?

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This data resource can scale from the state level down to the census tract in terms of facility locations. For data visuals it groups sites together so you can not get a comprehensive visualization of regulated sites beyond the neighborhood and census tract level.