Putting a Human Face on the Algorithm: Co-Designing Recommender Personae to Democratize News Recommender Systems

Algorithmic recommender systems are on the rise in various societal domains, including journalism. While they offer great promise by making useful selections of large content pools, they raise various ethical and societal concerns due to their alleged lack of transparency, diversity and agency. Especially in the news context, this has serious implications because access to information is crucial in democratic societies. In this article we empirically explore the idea of algorithmic recommender personae as a productive socio-technical solution to these problems. We present the results from a two-phased qualitative study with Dutch and Belgian news readers (N = 27) to 1) co-design potential news recommender personae by inductively discerning core news reading motivations and relevant features, and 2) evaluate the most promising personae on their usefulness. Results highlight three distinct recommender personae (Expert, Challenger and Unwinder) that correspond with news consumers' most salient reading motivations. We conclude that, in an increasingly automated future, allowing users more control and including them when designing recommender systems is key. With this study we hope that media organizations take up the challenge towards developing human-centered and responsible algorithmic systems that serve the public good.

Automated summary

This article explores the concept of algorithmic recommender personae as a potential solution to the lack of transparency, diversity, and agency in recommender systems. Through qualitative research, the researchers identified three distinct recommender personae types that align with news consumers' main reading motivations. The results highlight the importance of giving users more control and involving them in the design of recommender systems in an increasingly automated future.