From Good Intentions to Meaningful Impact: A Design Justice Approach for News Recommendation

News recommender systems are increasingly positioned as tools for enhancing public discourse, yet their design often reflects narrow commercial or technological goals and overlooks systemic inequalities. This paper introduces a design justice framework to critically examine how these systems encode, enact and reinforce power at multiple levels. Drawing on prior design justice work, we analyze news recommendation processes from an interdisciplinary perspective across micro (values), meso (networks of expertise), and macro (problem scopes) levels. We argue that while efforts to incorporate fairness and diversity into algorithms are important, they are insufficient without confronting the structural dynamics that shape design decisions. In response, we provide a set of actionable strategies to help researchers and practitioners implement design justice in news recommendation processes, shifting the focus from intentions to real-world impact. These strategies offer guidance for evaluating how systems distribute risks and benefits across different social contexts. Rather than proposing a perfect solution, we use design justice as an orientation, acknowledging that justice is iterative and contextual, and that design processes must balance ideals with practical constraints. Our contribution is a conceptual bridge between communication and computer science that enables a more just approach to news recommendation.