News-recommender systems, which automatically select the content of newsletters, personalized news apps or social-media news feeds are playing an increasingly critical role in helping users to filter and sort information. And as such are fulfilling a crucial role in democratic society. Data analytics and recommender systems are going to be more and more pivotal in deciding what kind of news the public does and does not see. Depending on their design, recommenders can either unlock the diversity of online information for their or lock them into so-called filter bubbles. The challenge for the development of diversity-sensitive recommenders is defining what diversity in recommendations actually means. Often conceptualised as a measure of variance or even serendipity, diversity is an inherently normative concept, deeply rooted in democratic theory and our ideas of what it means to live in a democratic society. Funded by the SIDN fonds, a team of legal scholars, communication and computer scientists from the University of Amsterdam and RTL have worked on a project that translates insights from democratic theory into concrete metrics that can help to assess the performance of news recommenders. Condensing a concept that is as vague and colourful as diversity into a number of concrete metrics is not a trivial task. In my keynote I would like to present some of our work, and draw some lessons for future work on 'diversity by design'.