Advocacy in Technology and Society/Community Data and COVID-19

Topic Summary
COVID-19 revealed several realities about the strength of community organizing and the frailty in federal design. The virus had a disproportionate impact on working class communities and communities of color in the US and this was further reinforced in the mapping and presentation of how these communities were displayed.

Much like the importance of representation in storytelling, when communities do not have access to the tools to voice their own lived realities, their experiences can be misrepresented and erased. Furthermore, inaccurate representations of a communities lived realities can be used to ensure that power and wealth are contained within the paradigms of whiteness (D’Ignazio & Klein, 2020). Just like storytelling, when communities are excluded from having access to tools to map and identify their lived realities, their experiences are threatened by erasure.

During the peak of the pandemic, individuals took it upon themselves to organize and visualize the ways in which the in-real-time impact could be accessed, portrayed, and engaged with. Untapped New York is an example of building interactive and visual feeds through open source coding to build a collaborative environment to share information using found data. Untapped New York in particular captured data by pulling information from NYC Open Data and the Department of Health. However, how do we know that these outlets have accurately captured data? What filters have they used to represent a holistic view of the impact and losses in communities that were further exacerbated by things like the digital divide and educatio inequity regarding reporting COVID-19 cases?

Untapped NYC shared the disclaimers in regards to how information was representing different communities:


 * The data in the map does not include probable deaths, which includes deaths with no known positive laboratory test for the virus that causes COVID-19, but the death certificate lists “COVID-19” or an equivalent as a cause of death. Probable deaths are being tracked by the Department of Health however, and over time, probable deaths may get recategorized as confirmed deaths.
 * The neighborhood names are assigned by the NYC Department of Health but they don’t always align with what residents term that area, so you may find yourself questioning some neighborhood designations (Young, 2020).

While these maps provide valuable information to engage with the visualization of data that continues to be collected and updated, they raise questions about the “danger of a single story” (Adichie, 2009). In her TedX Talk, Chimamanda Ngozi Adichie discusses the removal of unique details that occurs when a person is disempowered from the ability to share their own story."Power is the ability not just to tell the story of another person, but to make it the definitive story of that person. The Palestinian poet Mourid Barghouti writes that if you want to dispossess a people, the simplest way to do it is to tell their story and to start with, 'secondly.' Start the story with the arrows of the Native Americans, and not with the arrival of the British, and you have an entirely different story. Start the story with the failure of the African state, and not with the colonial creation of the African state, and you have an entirely different story."In class, we discussed the variations of data maps and the mobility of building accessible representations of data through community organized visualizations. The readings challenged the systems used to build technology and data-based practices.

Chapter 2: Collect, Analyze, Imagine, Teach
In their book, Catherine D’Ignazio and Lauren Klein highlight the histories of data mapping that are not founded in people-centric approaches. They share the dangers of inaccurate data visualization by providing historical evidence of policies, such as redlining, that manipulated data to uphold white power and wealth. To challenge these systems, D’Ignazio and Klein propose alternatives to “envision equity” and “imagine co-liberation” by drawing from abolitionist and community centric lenses in order to “assert the notion of algorithmic fairness” (D’Ignazio & Klein, 2020).

Chapter 6: The Numbers Don’t Speak for Themselves
In this chapter, D’Ignazio and Klein discuss the power surrounding open data as “free access, use, modify, and share data for any purpose” (D’Ignazio & Klein, 2020). However even as policies have encouraged the open access and free use of government organized data, building transparency by uploading documents for access has not come with the addition of information to provide context for the data. The lack of appropriate explanation, then, aggravates ways in which data can be used to manipulate “racism, sexism, and other forms of oppression” (D’Ignazio & Klein, 2020).

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A Review of COVID-19 Intersectional Data Decision-Making: A Call for Black Feminist Data Analytics ===== In their article, Kim Gallon of COVID Black discusses the importance of centering Black Feminism in the collection of design of data frameworks. Gallon emphasizes the importance of applying intersectional frameworks to recognize the “multiple identities” that an individual carries and the threat to diversity, equity, and inclusion efforts without this focus. Gallon provides an example of a case study to highlight the benefits of applying intersectionality frameworks, and the losses of not challenging design filters enough.

Insights
During the Covid period, data collecting has been an effective way to safeguard public health. However, data and privacy are inextricably linked. In the first year of Covid, some states—including California and Texas— weren't sharing vaccine-related ethnicity and racial data with the CDC, citing patient privacy laws. Furthermore, incomplete demographic information associated with other COVID-19 metrics pointed to the presence of racial and ethnic disparities (Antonios, Chatterjee, Gee, Kravitz, &Senese, 2021). In this case, the idea of public health is questioned, since the data of certain communities are missing.

Information could also do harm to people, and everyone should acknowledge information is a double-edged sword. There is a lot of information online and offline, but not everyone has adequate information literacy to process it. Accordingly, wrong information could undermine public responses, and disinformation can harm public health, which would disproportionately influence people as well. For example, the senior community, particularly seniors of color and LGBT older adults with a higher rate of poverty and illness are more vulnerable to disinformation, which may harm their physical health and cause a financial burden during the COVID. Besides, limited tech literacy further poses risks to people. Social media apps can be useful for sharing information about the outbreak, but there are fake apps claiming to track COVID cases, which is on the rise. In fact, they are often scams to infect and lock people’s devices and demand ransom (WebMD, 2020).

In addition, Covid-19 data is not equally accessible to everyone, considering diversity in the New York City, and over 50% of New Yorkers can't speak English very well or not at all. This disproportionately impacts immigrants who are facing language and cultural barriers but need an accessible system to keep informed.

What does advocacy look like here?
As highlighted in Data-Driven Approach to Addressing Racial Disparities in Health Care Outcomes by Kim Gallon, approaching data collection through the contributions of Black Feminism provides an opportunity to acknowledge the intersectionality of our “multiple identities” that impact data representation (Gallon, 2020). In order to further understand how COVID-19 was impact particular communities in Boston, a group or researchers, physicians, and health analysts at Brigham Health collected the following data from COVID-19 test spots: race, ethnicity, sex, language, insurance status, geographic location, and health-care worker status (Gallon, 2020). The aim of this research was to apply an inclusive approach by specifically incorporating Kimberlé Crenshaw’s intersectionality framework. In doing so, the body of researchers were able to identify that “Hispanic non-English speaking patients were dying at higher rates than Hispanic English speaking patients.” However even with the intersectional framework that was applied to collect data, the approach failed to “consider the ways that the algorithms used to filter the data may have reflected a matrix of domination.” Without defining the filters that were created, questions are raised about the ways in which, for instance, gender is defined or how race is simplified. While technology and data-based practices provide opportunities to amplify the impact of issues that organizers are advocating for or against, this example is an important reminder of how systems are reflections of the values, principles, and beliefs of the team designing it. When individuals are not considering the restraints of who is and isn’t included in the process of collecting data, equity is threatened by norms and standards that weren’t created to be inclusive.

Just as we challenge power narratives and structures in storytelling, it is important to challenge the power of mobilizing data by “pushing back against existing and unequal power structures and to work toward more just and equitable futures” (D’Ignazio & Klein, 2020). In Data Feminism, Catherine D’Ignazio and Lauren Klein provide alternatives to equitable and inclusive data mapping:


 * 1) Collect: Compiling counter-data—in the face of missing data or institutional neglect.
 * 2) Analyze: Challenging power often requires demonstrating inequitable outcomes across groups, and new computational methods are being developed to audit opaque algorithms and hold institutions accountable.
 * 3) Imagine: We cannot only focus on inequitable outcomes, because then we will never get to the root cause of injustice. In order to truly dismantle power, we have to imagine our end point not as “fairness,” but as co-liberation.
 * 4) Teach: The identities of data scientists matter, so how might we engage and empower newcomers to the field in order to shift the demographics and cultivate the next generation of data feminists?

Director and Co-Founder of COVID Black, Kim Gallon, also captured the importance of centering Black feminism in data analysis frameworks to consider who we are centering and challenge how we are normalizing power dynamics. She highlights the following definitions to challenge the collection of data:


 * Data feminism: concentrates on “intersecting forces of privilege and oppression” that demonstrate that power is not shared equally in society; minoritized identities intersect (Gallon, 2020).
 * Data justice: any notion of algorithmic fairness must also acknowledge the systematic nature of the unfairness that has long been perpetrated by certain groups on others.
 * Design injustice: (design processes that intentionally and unintentionally engender individual and systematic inequity) becomes more difficult to identify without applying the mutually constitutive Black feminist frameworks, intersectionality, and matrix of domination, to COVID racial data analysis (Gallon 2020).
 * Intersectionality: “the paradigm of sex discrimination tends to be based on the experiences of white women; the model of race discrimination tends to be based on the experiences of the most privileged Blacks.” Race and sex discrimination are constructed to address a narrow set of circumstances that deny Black women’s lived experiences (Gallon, 2020).
 * Matrix of domination: a sociological paradigm that explains issues of oppression that deal with race, class, and gender, which, though recognized as different social classifications, are all interconnected.The matrix of domination is a way for people to acknowledge their privileges in society. How one is able to interact, what social groups one is in, and the networks one establishes is all based on different interconnected classifications (Wikipedia).

Annotated References

 * Antonios, C., Chatterjee, M., Gee, G., Kravitz, D., & Senese, K. (2021, February). Analysis & Updates: Why some states won't share race and ethnicity data on vaccinations with the CDC-and why that's a problem. The COVID Tracking Project. Retrieved April 26, 2022, from https://covidtracking.com/analysis-updates/why-some-states-wont-share-race-and-ethnicity-data-on-vaccinations-with-the-cdc-and-why-thats-a-problem

This article talks about data-sharing issues at the state level. the Centers for Disease Control and Prevention asked every US state and territory to sign a contract, agreeing to share wide-ranging vaccine data   and records with the federal government. Several states raised concerns, but every one signed. But it turns out that most states ended up withholding the names, addresses, ZIP codes, and dates of birth of those vaccinated, and at least seven states go a step further and redact race and ethnicity data from the federal government or don’t collect it in the first place. Besides, this article also explores the rationale for withholding data.


 * Centers for Disease Control and Prevention. (2022). CDC Covid Data tracker. Centers for Disease Control and Prevention. Retrieved April 26, 2022, from https://covid.cdc.gov/covid-data-tracker/#demographicsovertime

This resource provides data on COVID-19 weekly cases and deaths per 100,000 population by age, race/ethnicity, and sex.
 * The Covid Tracking Project. The COVID Tracking Project. (2021). Retrieved April 26, 2022, from https://covidtracking.com/

This project tracked COVID-19 data since 2019, and provided a comprehensive, relatively standardized data system to support individuals. But this project stopped collecting data in 2021.


 * The Danger of a Single Story. (n.d.). Chimamanda Ngozi Adichie: The danger of a single story | TED Talk. Retrieved April 26, 2022, from https://www.ted.com/talks/chimamanda_ngozi_adichie_the_danger_of_a_single_story.

In this video, author Chimamanda Ngozi Adichie emphasizes the importance of writers to share their own stories to represent their communities with accurate representations of lived experiences and histories. Adichie discusses the dangers and threats that communities face when those outside of them enter to tell their versions of stories. She stresses the importance of correcting histories through the act of storytelling.
 * D'Ignazio, C., & Klein, L. (2020, March 16). 2. collect, analyze, imagine, teach · data feminism. Data Feminism. Retrieved April 26, 2022, from https://data-feminism.mitpress.mit.edu/pub/ei7cogfn/release/4

This chapter examines power applying an intersectional framework to reveal and acknowledge intersecting identities in data collection and data analysis. power and inequity are structured differently for different groups. Then, authors explore ways to challenge power, like collecting counter-data to quantify and visualize structural oppression, as Gwendolyn Warren and the DGEI did with their maps. This helps people who occupy positions of power understand the scope, scale, and character of the problems from which they are otherwise far removed.


 * D'Ignazio, C., & Klein, L. (2020, March 16). 6. the number's don't speak for themselves · data feminism. Data Feminism. Retrieved April 26, 2022, from https://data-feminism.mitpress.mit.edu/pub/ei7cogfn/release/4

This chapter examines the phenomenon of open data and its relationship to the impact of availing raw data without additional information. The authors discuss ways in which raw data can be used to spread misinformation and manipulate forms of oppression. The authors encourage continuing to build access to open data, while also providing information in which the datas are seated in, to avoid the "reinforcement of unjust status quo."
 * Gallon, Kim. A Review of COVID-19 Intersectional Data Decision-Making: A Call for Black Feminist Data Analytics, Part I https://medium.com/@ktgallon/a-review-of-covid-19-intersectional-data-decision-making-a-call-for-black-feminist-data-analytics-da8e12bc4a6b (Links to an external site.)

The author analyzes the intersectional approach to COVID-19 and talks about the significance of acknowledging people's multiple identities, which shape their vulnerability and experience with the coronavirus. Besides, this intersectional approach to data analysis is effective to develop strategies for ameliorating the impact of COVID-19 on Black and Brown people. However, the article also reveals the limits of this approach and discusses further algorithms of oppression, which is a specific manifestation of discriminatory design in which racist values and assumptions are built into technical systems.


 * Sivashanker, K., Duong, T., Ford, S., Clark, C., & Eappen, S. (2021, February 1). A data-driven approach to addressing racial disparities in health care outcomes. Harvard Business Review. Retrieved April 26, 2022, from https://hbr.org/2020/07/a-data-driven-approach-to-addressing-racial-disparities-in-health-care-outcomes

This research article shared the results of a case study taken place at Brigham Health in Boston to present COVID-19's disproportionate impact on Indigenous, Black, and other disadvantaged communities. The study illuminates an attempt made to center Black Feminist frameworks in a data collection model by using intersectionality to set the parameters for filters that would organize data. The study was used to reinforce the health inequities that are exacerbated when the collection of data is not organized to reflect on racial, ethnic, and class disparities.
 * WebMD. (2020, August 17). Mobile apps for Coronavirus (COVID-19): See the list. WebMD. Retrieved April 26, 2022, from https://www.webmd.com/lung/coronavirus-apps#1

This article introduces coronavirus mobile apps, like coronavirus symptoms apps, Covid contact tracing apps, health monitoring apps, social distancing apps, and Covid prevalence apps. Besides, it also talks about risks when people use these Covid apps. For example, the app can access personal information on one's device, but sometimes the rationale for this might not make sense for the app's purpose.


 * Wikimedia Foundation. (2022, February 9). Matrix of domination. Wikipedia. Retrieved April 26, 2022, from https://en.wikipedia.org/wiki/Matrix_of_domination#cite_note-:0-3

This source was used to help define the contributions of Patricia Hill Collins to emphasize another framework to center Black Feminism in data.
 * Young, M. (2021, August 17). Interactive map of COVID cases shows NYC's hotspot ZIP codes. Untapped New York. Retrieved April 26, 2022, from https://untappedcities.com/2020/11/12/interactive-map-covid-nyc-hotspot-zip-codes/

This article profiles an interactive data visualization map that was published in 2020 to track hotspot COVID cases using NYC open data sources. The interactive map uses color gradients to visualize COVID cases in different zip codes across NYC captured by testing sites, and other city collected data. While the visualization provides a valuable resource for city residents to engage with health and safety concerns in their neighborhoods, the profile also underscores gaps in data that prevent the map from accurately shaping certain communities.