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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.

Newark's air is polutted from the factories and warehouses the most. Also, Newark's port is surrounded by factories and use of toxic gases. I think the air pollution in Newark impacts the residents of Newark and the surrounding cities as well. Air Pollution causes lots of diseases.

First of all I would like to highlight the first source used in the new. The map with the risk on air polution in Newark.

Now I'll point out the two qutes suggested:

"Air quality was analyzed using proximity to 5 factors: major roads, truck routes, rail lines, Newark airport are all nonpoint sources and facilities that have violated their major permit at least once within the last 3 years are point sources. Point sources were buffered 1 miles for the area of high risk, and 1.5 miles for the area of elevated risk."

(at the begging of the last paragraph)

"This project is an attempt to identify those areas of high risk and the people being affected by poor air quality. It can be used to inform the public about their risk and to influence policy makers and developers."

(the fourth paragraph)

In an industrial city like Newark, although prevention of air pollution is hard, but control can be in our hands. By identifying the areas with the higher risks and also the people being affected by the poor air quailty, we can further give the community more clear information regarding the risks and also in turn influencing policy makers and the stakeholders of the community. Being correctly informed on the topic not only helps the community members, but also the people in charge of making decisions for their communities, making this a better way to work together to build a healthier ans safer community in areas like these. 

Yes, individual communities should determine the air quality standards for their areas because each area would have a different standard. For example the Newark area air quality standard would be much lower then the other tri-state areas of New Jersey. If Newark's air quality was measured with standards that are kept for the whole state then the results would be much lower and wouldn't be right to compare the two. Having different standards per each area helps in diffrentiating between each one.