Political microtargeting, and the conditions under which the use of AI and data analytics can contribute to, or threaten digital democracy are questions of central academic, societal and political importance. Central challenges to digital democracy are the lack of (1) a theoretical assessment framework, (2) the need for new methods to generate empirical insights into existing practices and their effects on users and society and 3) the lack of concrete suggestions of how to move the discussion from the current, very abstract level to discussing concrete regulatory and policy solutions.
This research will tackle all three challenges through a unique synthesis of four sets of expertise (political philosophy, law, political communication and computer science) and a mix of sophisticated research methods (including experiments, a survey, digital tools and data analytics) to study political microtargeting on Facebook as a case study. Using democratic theory, a concrete set of legitimacy conditions will be produced that need to be fulfilled for political microtargeting to contribute, rather than harm digital democracy.
The insights from the normative analysis will be complemented, and tested by empirical research into actual practices and effects on voters. Using innovative digital tools, the 'black box' will be unlocked to make the extent and dynamics of political microtargeting visible. Together, these insights will feed into the legal analysis to develop much-needed legal and policy solutions for governing political microtargeting.