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Abstract
Mass surveillance is a more real threat now than at any time in history. Digital communications and automated systems allow for the collection and processing of private information at a scale never seen before. Many argue that mass surveillance entails a significant loss of privacy. Others dispute that there is a loss of privacy if the information is only encountered by automated systems.
This paper argues that automated mass surveillance does not involve a significant loss of privacy. Through providing a definition of informational privacy as a matter of actual access of private information by one who can understand the meaning of that information, it follows that automated systems which lack understanding cannot of themselves diminish privacy. This is not to say that mass surveillance is unproblematic, though: it is deeply problematic. It is just that privacy is not the most significant of these problems.

There is a widespread belief that machine learning tools can be used to improve decision-making in health and social care. At the same time, there are concerns that they pose threats to privacy and confidentiality. Policy makers therefore need to develop governance arrangements that balance benefits and risks associated with the new tools. This article traces the history of developments of information infrastructures for secondary uses of personal datasets, including routine reporting of activity and service planning, in health and social care. The developments provide broad context for a study of the governance implications of new tools for the analysis of health and social care datasets. We find that machine learning tools can increase the capacity to make inferences about the people represented in datasets, although the potential is limited by the poor quality of routine data, and the methods and results are difficult to explain to other stakeholders. We argue that current local governance arrangements are piecemeal, but at the same time reinforce centralisation of the capacity to make inferences about individuals and populations. They do not provide adequate oversight, or accountability to the patients and clients represented in datasets.