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The Class Differential in Big Data and Privacy Vulnerability

Introduction:

Low-income communities have historically been subject to a wide range of governmental monitoring and related privacy intrusions in daily life. The privacy harms poor communities and their residents suffer as a result of this pervasive surveillance are especially acute when considering the economic and social consequences they experience, and the low likelihood that they will be able to bear the costs associated with remedying those harms. In the “big data” era, there are growing concerns that low-status internet users may be further differentially impacted by certain forms of internet-enabled data collection, surveillance, and marketing. They may be both unfairly excluded from opportunities and unfairly targeted based on determinations made by predictive analytics and scoring systems—growing numbers of which rely on some form of social media input. These 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, could have particularly negative impacts on the poor.

In addition to the harms created by targeting (e.g., predatory marketing) or exclusion from opportunity, the poor may face magnified privacy vulnerabilities as a result of community-specific patterns around technology use, and knowledge gaps about privacy- and security-protective tools. Legal scholars have identified a broad group of consumers as “privacy vulnerable” when they “misunderstand the scope of data collection and falsely believe that relevant privacy rights are enshrined in privacy policies and guaranteed by law.” These misconceptions are common across all socioeconomic categories, but this article suggests that these conditions may be exacerbated by poor communities’ higher reliance on mobile connectivity and lower likelihood to take various privacy-protective measures online. When low-income adults rely on devices and apps that make them more vulnerable to surveillance, and they wittingly or unwittingly do not restrict access to the content they post online, they may be further exposed to forms of commercial data collection that can affect the way they are assessed in various employment, education and law enforcement contexts.

Part I of this article provides a historical overview of the ways in which the poor have been subject to uniquely far-reaching surveillance across many aspects of life, and how their experiences of harm may be impacted by evolving practices in big-data-driven decision making. In using the term “poor” to signify a condition of economic deprivation, this article recognizes that low-income people in America are a diverse and multifaceted group and that each person has his or her own individualized narrative. Despite this diversity, this article highlights a shared reality for many poor people, which is heightened vulnerability to on-line surveillance and associated adverse outcomes.  Part II presents new empirical findings from a nationally representative survey to highlight various technology-related behaviors and concerns that suggest low-status internet users may be especially vulnerable to surveillance and networked privacy-related harms. In Part III, we show why and how this matters through a legal examination of several timely case studies that demonstrate how on-line activity, and the emerging use of  social media data in particular, might have detrimental impacts on the poor when used in high-stakes decision-making systems. This Part explains why current legal frameworks fail to shield the poor from negative outcomes. Finally, in Part IV, we assess major proposals for protecting on-line, personal data through the lens of class vulnerability.  In other words, we evaluate how these proposals might impact poor people.  We agree with other scholars that additional technical and non-technical reforms are needed to address the risks associated with the use of social media data.  As policymakers consider reforms, we urge greater attention to impacts on low-income persons and communities.