Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems

Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection […]

Surveillance in Europe

Surveillance in Europe is an accessible, definitive and comprehensive overview of the rapidly growing multi-disciplinary field of surveillance studies in Europe. Written by experts in the field, including leading scholars, the Companion’s clear and up to date style will appeal to a wide range of scholars and students in the social sciences, arts and humanities. […]

Constructing a surveillance impact assessment

This paper describes surveillance impact assessment (SIA), a methodology for identifying, assessing and resolving risks, in consultation with stakeholders, posed by the development of surveillance systems. This paper appears to be the first such to elaborate an SIA methodology. It argues that the process of conducting an SIA should be similar to that of a […]