Algorithms are used across a wide range of societal sectors such as banking, administration, and healthcare to make predictions that impact on our lives. While the predictions can be incredibly accurate about our present and future behavior, there is an important question about how these algorithms in fact represent human identity. In this paper, we explore this question and argue that machine learning algorithms represent human identity in terms of what we shall call the statistical individual. This statisticalized representation of (...) individuals, we shall argue, differs significantly from our ordinary conception of human identity, which is tightly intertwined with considerations about biological, psychological, and narrative continuity—as witnessed by our most well-established philosophical views on personal identity. Indeed, algorithmic representations of individuals give no special attention to biological, psychological, and narrative continuity and instead rely on predictive properties that significantly exceed and diverge from those that we would ordinarily take to be relevant for questions about how we are. (shrink)

The United Nations confirmed that privacy remains a human right in the digital age, but our daily digital experiences and seemingly ever-increasing amounts of data suggest that privacy is a mundane, distributed and technologically mediated concept. This article explores privacy by mapping out different legal and conceptual approaches to privacy protection in the context of datafication. It provides an essential starting point to explore the entwinement of technological, ethical and regulatory dynamics. It clarifies why each of the presented approaches emphasises (...) particular aspects and analyses the tensions that arise. The resulting overview provides insight into the main strengths and limitations of the different approaches arising from specific traditions. This analytic overview therefore serves as a key resource to analyse the usefulness of the approaches in the context of the increasing datafication of both private and public spheres.Specifically, we contrast the approach focusing on data subjects whose data are being ‘protected’ with others, including Fair Information Practice Principles, the German right to ‘informational self-determination’, and the South American ‘habeas data’ doctrine. We also present and contrast emerging approaches to privacy and discuss their intersection with datafication. In conclusion, we put forth that rather than aiming for one single solution that works worldwide and across all situations, it is essential to identify synergies and stumbling blocks between the various regulatory settings and newly emerging approaches. (shrink)