Privacy, Poverty and Big Data: A Matrix of Vulnerabilities for Poor Americans

68 Pages Posted: 11 Mar 2017  

Mary Madden

Data & Society Research Institute; Harvard University - Berkman Klein Center for Internet & Society; Pew Research Center (Past Affiliation)

Michele E. Gilman

University of Baltimore - School of Law

Karen EC Levy

Cornell University

Alice E. Marwick

Data & Society Research Institute

Date Written: March 9, 2017

Abstract

This Article examines the matrix of vulnerabilities that low-income people face as a result of the collection and aggregation of big data and the application of predictive analytics. On the one hand, big data systems could reverse growing economic inequality by expanding access to opportunities for low-income people. On the other hand, big data could widen economic gaps by making it possible to prey on low-income people or to exclude them from opportunities due to biases that get entrenched in algorithmic decision-making tools. New kinds of “networked privacy” harms, in which users are simultaneously held liable for their own behavior and the actions of those in their networks, may have particularly negative impacts on the poor. This Article reports on original empirical findings from a large, nationally-representative telephone survey with an oversample of low-income American adults and highlights how these patterns make particular groups of low-status internet users uniquely vulnerable to various forms of surveillance and networked privacy-related problems. In particular, a greater reliance on mobile connectivity, combined with lower usage of privacy-enhancing strategies may contribute to various privacy and security-related harms. The article then discusses three scenarios in which big data – including data gathered from social media inputs – is being aggregated to make predictions about individual behavior: employment screening, access to higher education, and predictive policing. Analysis of the legal frameworks surrounding these case studies reveals a lack of legal protections to counter digital discrimination against low-income people. In light of these legal gaps, the Article assesses leading proposals for enhancing digital privacy through the lens of class vulnerability, including comprehensive consumer privacy legislation, digital literacy, notice and choice regimes, and due process approaches. As policymakers consider reforms, the article urges greater attention to impacts on low-income persons and communities.

Keywords: privacy, poverty, big data, social media, employment, policing, education, digital literacy

Suggested Citation

Madden, Mary and Gilman, Michele E. and Levy, Karen EC and Marwick, Alice E., Privacy, Poverty and Big Data: A Matrix of Vulnerabilities for Poor Americans (March 9, 2017). Washington University Law Review, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2930247

Mary Madden

Data & Society Research Institute ( email )

36 West 20th Street
New York,, NY
United States

HOME PAGE: http://https://datasociety.net/people/madden-mary/

Harvard University - Berkman Klein Center for Internet & Society ( email )

Harvard Law School
23 Everett, 2nd Floor
Cambridge, MA 02138
United States

Pew Research Center (Past Affiliation) ( email )

1615 L St N.W.
Suite 700
Washington, DC
United States

Michele E. Gilman (Contact Author)

University of Baltimore - School of Law ( email )

1420 N. Charles Street
Baltimore, MD 21218
United States

Karen EC Levy

Cornell University ( email )

Ithaca, NY 14853
United States

Alice E. Marwick

Data & Society Research Institute ( email )

36 West 20th Street
New York,, NY
United States

HOME PAGE: http://www.datasociety.net

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