Feminist theories have extensively debated consent in sexual and political contexts. But what does it mean to consent when we are talking about our data bodies feeding artificial intelligence (AI) systems?

Re-imagining data protection with an Afrofuturist data subject to counter digital racism and reclaim digital humanity.

This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. It argues that a clear distinction must be made between different concepts of bias in such systems in order to analytically assess and politically critique these systems. By analysing the controversial Austrian “AMS algorithm” as a case study among other examples, this paper defines the following three types of bias: purely technical, socio-technical, and societal.

What do we mean when we say data collection? This paper enacts a performative and feminist critique of the term and discuss potential alternatives.

This analysis of digital technologies aimed at supporting survivors of sexual and gender-based violence illustrates how they reaffirm normative whiteness.

The UK welfare system relies on gender stereotyping and, increasingly, on surveillance: risking a vicious cycle of categorisation and control

Apps to help track fertility and menstruation proliferate, and so does the commodification of user data. How is this tendency viewed in light of the GDPR and the UCPD?

Drawing from science and technology studies (STS) and a feminist law critique, this article argues that procedural law is insufficient when addressing algorithmic discrimination and that ex ante protection may be a better way forward.

Sexism and racism intersect in the most recent debate on police use of DNA technology, building on right-wing populist sentiments.