This postdoctoral project investigates how artificial intelligence and recommender algorithms shape citizens’ news consumption and the distribution of political information online. The research explores whether public service algorithms can counteract informational inequality by ensuring that all users, not only the most motivated, are exposed to relevant news and democratic content.
Using advanced computational modelling, behavioural data, and large-scale simulation, the project develops and tests new approaches to algorithmic curation that balance attention optimisation with democratic objectives. By integrating insights from political communication, computer science, and human–algorithm interaction, the project aims to advance both the conceptual understanding and the practical design of AI systems that support an informed and equitable public sphere.
Member and main contact: Rasmus W. Schmøkel
Using advanced computational modelling, behavioural data, and large-scale simulation, the project develops and tests new approaches to algorithmic curation that balance attention optimisation with democratic objectives. By integrating insights from political communication, computer science, and human–algorithm interaction, the project aims to advance both the conceptual understanding and the practical design of AI systems that support an informed and equitable public sphere.
Member and main contact: Rasmus W. Schmøkel