Distributional Data Justice: Shifting Policy to Address Digital Discrimination in Vulnerable Communities
Posted: 16 May 2019
Date Written: April 19, 2019
Vulnerable communities appear most likely to be victimized by Big Data discrimination. As a result, policy prescriptions aiming to deliver privacy and reputation protections should be directed at those most likely to be affected. Such a strategy requires a fundamental shift in regulatory philosophy. The current utilitarian approach of fair information practice, exemplified by the failures of the notice and choice model, should be supplemented, if not replaced, by an approach rooted in distributional justice, which emphasizes a more pragmatic and equitable system. Such an approach can be developed by relevant application of the philosophies of John Rawls and Amartya Sen to media and communication policy.
Utilitarian regulatory philosophy, which has dominated Western policy approaches since the development of modern nationhood, prioritizes the well-being of the aggregate over the necessities of the individual. Utilitarian approaches tend to be goal-oriented as opposed to rights-based, and emphasize the sum of individual welfares, or the common-good, as opposed to the needs of the least fortunate.
This one-size-fits-all approach is exactly how the notice and choice framework operates, and in part, why it fails. For example, current scholarship suggests that consent models are broken due to an inability to deliver consistent understanding and engagement of privacy policies. Similar challenges persist with the choice component. Nevertheless, governments continue to champion empty words that fail to deliver protections. Indeed, the blanket utilitarian approach does not address how the differences between individuals create a mosaic of unique data-privacy self-management challenges, and ignores the threat imbalance.
Rawlsian approaches to distributional justice emphasize the need to minimize the influence of individual differences in order to produce a more just society. These approaches suggest societal institutions must emphasize: 1) the basic liberties of each person, and 2) that inequalities should be arranged to the benefit of all, ensuring that the least advantaged are provided the greatest advantage. How to achieve Rawlsian distributional justice is taken up by Sen, who argues for the “capabilities approach,” which suggests that prescriptions for weeding out societal disadvantages should not emphasize societal “design”, but rather differences in ability to overcome disparities. Such an approach has led policy scholars to suggest that policy instruments should bolster educational and other support structures targeted at improving the opportunities of vulnerable individuals and communities to turn desired functionings into achievable capabilities.
Advancing a distributive theory of data justice, this paper will propose a variety of policy prescriptions directed at vulnerable communities most likely to be the focus of digital discrimination. The development and support of info-mediation services designed specifically to offer principal-agent delegation benefits to those communities will be offered as a primary strategy. Investment in educational campaigns to advance digital citizenship in vulnerable communities will also be discussed as a strategy for achieving digital privacy and reputation deliverables.
Keywords: Big Data, Privacy, Data Justice, Infomediation
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