Data-Driven Personalisation in Markets, Politics and Law

Abstract

This is the introductory chapter to the edited collection on 'Data-Driven Personalization in Markets, Politics and Law' (CUP, 2021) that explores the emergent pervasive phenomenon of algorithmic prediction of human preferences, responses and likely behaviors in numerous social domains and subsequent ‘implementation’ - ranging from personalized advertising and political microtargeting to precision medicine, personalized pricing and predictive policing and sentencing. This chapter reflects on such human-focused use of predictive technology, first, by situating it within a general framework of profiling and defends data-driven individual and group profiling against some critiques of stereotyping, on the basis that our cognition of the external environment is necessarily reliant on relevant abstractions or non-universal generalizations. The second set of reflections centers around the philosophical tradition of empiricism as a basis of knowledge or truth production, and uses this tradition to critique data-driven profiling and personalization practices in its numerous manifestations.

Keywords: data-driven personalization, political micro-targeting, privacy, data privacy, stereotyping, discrimination, equality, predictive policing, predictive sentencing, racial discrimination, gender discrimination, truth production, personal autonomy, presumption of innocence, precision medicine