FairNews

FairNews: News in a Big Data Age

The news industry is in rapid transformation as users increasingly consume their news online and in personalized ways. News personalization is enabled by algorithms that select stories based on people’s online behavior, but how these algorithms work and what consequences they have for the public sphere is often unknown. While algorithmic curation allows media organizations to be more receptive to the needs of their audience, it also poses considerable societal challenges. News personalization can lead to information asymmetries as some social groups might structurally be deprived from certain news, making it hard for these groups to participate in democratic debates. Moreover, algorithmic curation may influence users’ news consumption habits as they know that their online behavior is being “watched” by algorithms.

In this multi-disciplinary research project we explore what fairness means in an algorithmically saturated news environment and how to counter possible biases created through news personalization. This complex issue raises important questions about human rights, such as the freedom of expression and the right to receive information, but also other ethical issues, such as what user data can news organizations legitimately collect and how consumers should be informed about algorithmic news curation. We also examine how people consume news in the algorithmically curated news environments and whether personalization leads to information bubbles. Based on our analysis, we develop tools to mitigate societal issues arising from the use of news personalization and address broader political and normative considerations about what democratic ideals we assign to the media.

Team