Aligning Algorithms with Axiology: Operationalizing Journalistic Values in News Recommender Systems

Abstract

Recommender systems shape how people shop, learn, and read the news, and advances in algorithms have made personalization increasingly influential. Yet most systems are optimized for accuracy and engagement without asking the normative question of what recommender systems should be. When we fail to ask this question, we risk systems that amplify misinformation, reinforce biases, and create echo chambers that affect both individuals and society at large. News recommendation is a powerful case in point: while it plays a pivotal role in shaping public opinion, current systems lack mechanisms to include journalists’ voices, values, and perspectives. My work addresses this gap through (1) a co-design methodology for eliciting and encoding journalistic values, (2) a values-aware reranking instantiation built on SCRUF-D, and (3) longitudinal field evaluation via POPROX. This work reimagines recommender systems not merely as engines of engagement, but as accountable infrastructures that uphold democratic values.