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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.

(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.

There are missing data points within the dataset (attributed to non-reported information). This dataset has also been acknowledged to be limited in its prioritization of government data, which could have political limitations that may skew the degree of severity for disasters reported. 

This dataset can be used to demonstrate the geographic distribution of disasters in Vietnam over time. This database recognizes multiple dimensions of disaster, including natural (typhoons, hurricanes), technological (a chemical spill, a factory explosion), and more

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This dataset includes information at the country and regional level. Reports of disasters, both natural and human-related, are recorded at the country and province, region, or city level.  

This resource has been used in a publication written by Hoang et al., 2018 on the economic cost of the Formosa Toxic Waste Disaster in Central Vietnam. It is specifically used within the journal article to highlight the forms in which disasters can take place within a nation, and the rising cases of industrial disasters that have afflicted vulnerable communities within the last decade. This characterization sets the stage and context for the Formosa disaster, and integrates it within a wider conversation about the effects of intensified industrialization on the environment. 

These datasets all involve  a strong spatial component. The presentation of such data could best be done via GIS Software, with their integration within a story map to demonstrate the importance of environmental stewardship to natural environments as well as the people who depend on such resources for their livelihoods.  For example, EPI data can be incorporated with EM-DAT’s disaster data to better understand the relationship between  a country’s EPI performance and the amount of technological disasters it observes. A country’s EPI score on Fish Stock Status can be compared with how much the nation’s GDP relies on fisheries to draw attention to discrepancies between stewardship and a country’s reliance on this resource. This process will require a user to be familiar with GIS Software and spatial plotting of data points (as the datasets themselves have not been integrated into ArcGIS), and using this software to integrate information together into meaningful maps.

[Source: EM-DAT Public] This graphic shows the prevalence of technological disasters [includes toxic spills, industrial explosions, etc.] by country. This can be used to characterize, on a transnational level, where potential industrial harms are centralized or concentrated. While it does not characterize more insidious harms, such as air pollution, it can be a direct and easy to understand measure of environmental harm distribution across the globe. 

Additionally, data is available as excel sheets, which allows users to produce their own graphics on the prevalence of disasters within a particular nation over a desired time interval. 

This was developed in 1988 by personnel from the Center for Research on the Epidemiology of Disasters (CRED) within the Université catholique de Louvain (UCLouvain) with funding from the Belgian government and the World Health Organization (WHO), this data source aims to provide free open access information for users affiliated with academic organizations, non-profits, and international public organizations looking to gain understanding on the distribution  of disaster occurrences around the globe.

The EMT disaster database is compiled from a wide variety of sources, including UN agencies, NGOs, insurance companies, research institutes, and press agencies. The dataset compilation process prioritizes data from UN agencies, the International Federation of Red Cross and Red Crescent Societies, and government agencies. Entries are reviewed prior to consolidation, and this process of checking and incorporating data is done on a daily basis. More routined  data checking and management also occurs at a monthly interval, with revisions made at the end of each year.