Laurens Naudts, AI, Media & Democracy Lab, University of Amsterdam
EU policy interventions that reflect inclusion as their ambition (e.g., standardisation bodies, regulatory sandboxes, and digital twins) and the instruments in which they are promoted (e.g., AI Act (2024), Apply AI strategy (2025)), appear grounded in a democratic model where the legitimacy of AI governance is measured based on active engagement, public expressions, and quantifiable representation. However, in foregrounding visibility as legitimacy, the political power of the invisible risks becoming obscured. Scholars have identified how oppressed individuals and social groups strategically deploy invisibility to escape and resist discriminatory institutionalisations of voice and identity. (Lollar, 2015; Smith et al., 2018; Bettivia, 2019; Alloa, 2023). Similarly, glitch feminism has explored how, within our digital age, the "in-between" within traditional dichotomies (e.g., virtuality/reality, the gender binary, the visible/invisible, etc.) may offer "new possibilities of being and becoming manifest" (Russell, 2020). These accounts detail how political agency can be found beyond what is immediately perceptible. Drawing on critical theory and glitch feminism, this talk explores how invisibilities can be used to strategically counteract structural inequalities within the AI value chain. At the same time, it examines how a politics that foregrounds visibility and legibility may undermine such resistance strategies and, in so doing, limit the navigational space within which civil society acts as a democratic counterpower.
A short bio:
Laurens Naudts is a Postdoctoral researcher at the AI, Media and Democracy Lab and Institute for Information Law (University of Amsterdam) and Research Fellow at KU Leuven CiTiP. He is also a visiting research fellow at the AI Policy Lab (Umeå University).
His research focuses on the regulation of artificial intelligence and information technology, digital fundamental rights, and the emergence of (structural) social injustice in the data-driven society.