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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.

“...Toxic Wastes and Race at Twenty (Bullard et al., 2007) revealed that communities of colour and poor communities were still being used as dumping grounds for all kinds of toxic contaminants. The authors discovered evidence that the clustering of environmental hazards, in addition to single sources of pollution, presented significant threats to communities of colour. Furthermore, the research showed that polluting industries frequently singled out communities of colour in siting decisions, countering the “minority move-in hypothesis”: the claim that people of colour voluntarily move into contaminated communities rather than being targeted in situ by dirty industries.” (122)

“Bullard (1990) has highlighted the problem of “Black Love Canals” throughout the United States, where issues of environmental injustice are deeply connected with environ- mental racism. For example, Bullard highlights the case of toxic DDT water contamination in the African American community of Triana, Alabama. In 1978, in the midst of the national media attention focused on Love Canal, residents in Triana raised complaints over ill-health effects and contaminated fish and waterfowl. Lawsuits in Triana against the Olin Corporation continued throughout the 1980s. Although the case is noted within environ- mental justice histories (see Taylor, 2014), it is not widely recognized or commemorated.” (126)

“Underpinning the slow, structural violence (see Galtung, 1969; Davies, 2019) of unequal and unjust toxic exposures is the problem of “expendability” … Pellow (2018) proposes that indispensability is a key pillar of critical environmental justice studies (alongside intersectionality, scale, and state power). This idea builds on the work of critical race and ethnic studies scholar John Marquez (2014) on “racial expendability” to argue that, within a white-dominated society, people of colour are typically viewed as expendable.” (127)

“National and international media headlines followed the Flint water crisis story as it unfolded, but, after the initial shock, Flint faded from media attention. It shifted from being a spectacular disaster to a case of slow violence. This paral- lels the dynamics of public memory surrounding many toxic disasters, struggles, and legacies.” (128)

The author’s main argument is two-fold. Acute environmental disasters (e.g., Chernobyl, BP Horizon Spill, Hurricane Katrina) that garnered public attention leave behind legacies of increased support for environmental action and legislation, although the public attention span is often too short for lasting change. At the same time, these disasters have received a disproportionate amount of public attention compared to the many more slow-moving toxicity disasters that affect people in more systematic but often less visible ways. Examples of this disparity include the contrast between the 1984 Bhopal disaster coverage, and the persistent toxicity in the area in the time since then in the form of industrial waste and infrastructure that is not maintained. It is additionally important to note that the cases that don’t receive much attention often affect marginalized groups (by race, socioeconomics) disproportionately.

In the article, the authors used data from the 2011-2015 American Community 5-Year Estimates by the U.S. Census, 2010 U.S Census, and George C. Galster, “The Mechanism(s) of Neighborhood Effects: Theory, Evidence, and Policy Implications.”. They looked at data follwing children under 18,  and followed poverty trends such as census tracts for concentrated areas of high poverty. They used the number of children in Essex County Cities and compared it to the the amount of children in poverty in those cities, for the years of 2000 and 2015. Henceforth, they created an arguement stating that Child Poverty rates have risen within those 15 years, and even by 50% in some areas. The only issue I have with some of this data is that in some cities, we see a decrease in child population - and while there is an increase in child poverty in those areas, I feel like the reduced number of children in that area plays a big part in the so called "Increased Child Poverty Rates".

The main argument in this artical is children poverty in newark has  increased by 19 percent  in the last 15 years. In newark over 40% of chlidren live in poverty. 

Vulnerability and resilience in this artifact are defined by the high concentration of child poverty in Essex County. They are measured using the Census from 2000 and 2015 which show how there is a trend in the percentage of children who expereince poverty within the county. Majority of the children living in poverty are currently living in heavilyu concentrated poverty neighborhoods like Newark, Irvington, and the Oranges. Although the affluent town of Milburn is nearby, it is unclear how these children continue to live below the poverty line in Newark even though the towns are only 6 miles apart. 

As a sesearch from the Rotgers University, the students or researchers support:

- The child poverty in becoming more concentrated. With the numbers next to us, we can say that a 52.5% of the poorest childs live in census were the concentration is above a 40%

- Inner-ring suburbs of Orange, East Orange, and Irvington have seen the largest increases in child poverty.

- Essex County’s smallest municipalities have very low child poverty, although many have seen their child poverty rates increase by more than 50 percent since 2000