One of the main topics in contemporary debates on the social impact of information technology is the creation and maintenance of huge digital archives through the observation of human activity by means of various forms of surveillance. Such data troves, in turn, are leveraged for decision-making in many different public and private environments. The issues of note, therefore, pertain not only to the problem of who is entitled to search and access the myriad digital repositories (and how), but also more generally to the way in which whole aspects of social interaction are completely transformed by this informational overload and the rise of data science as the unifying explanatory methodology of the age.

Specifically, the existence of these archives and of the practices of surveillance that make them possible call into question the trustworthiness of their creators and owners in three different ways. First, they bring to the fore issues of definition of data ownership and data sovereignty, hence of the essence itself of the targets of these practices: the fact that much of this data collection happens in opaque or hidden ways is at the heart of the trust deficit. Second, these data agglomerations are linked with algorithmic decision-making and the responsibilities (or lack thereof) it entails: the possibility for those who control the data to act strategically in order to influence the decision-making based on it inevitably produces a mistrust dynamic. Third, surveillance-generated electronic archives more basically redefine the boundaries between the public and the private in all sorts of social endeavors, therefore also the expectations of privacy and anonymity: trust in the non-invasiveness of all manner of IT is fundamentally called into question.

The project seeks to explore these themes through quantifications of the digital space (data about data), including studies of the ease of data collection and retention (comparative costs over time, etc.) and the long-term proliferation of personal information over our lifetimes. Case studies will be sought of the irruption of data science and algorithmic decision-making in particular fields. The trust implications at all levels of public versus private databases will be explored. Expert opinion will be collected, chiefly among data scientists and privacy experts.

Basic references:

• O’Neil, C., 2016. Weapons of math destruction: how big data increases inequality and threatens democracy. Crown, New York.
• Couldry, N., Mejias, U.A., 2019. The costs of connection: how data is colonizing human life and appropriating it for capitalism, culture and economic life. Stanford University Press, Stanford, California.
• Bauman, Z., Lyon, D., 2013. Liquid surveillance: a conversation. Wiley, Oxford.