Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity
By Judith Möller, Damian Trilling, Natali Helberger, Bram van Es. 2018
293 citations recorded by the source corpus.
ABSTRACT In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.
Subfields: Communication, Sociology and Political Science, Recommender Systems, AI Ethics, Fairness & Society
How researchers see this paper
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In Communication
In Sociology and Political Science
- William S. Isaac
- Iason Gabriel
- Borja Balle
- Myra Cheng
- Lisa Anne Hendricks
- T. Stepleton
- Atoosa Kasirzadeh
- Abeba Birhane
In AI Ethics, Fairness & Society
Citations in the corpus
- Ethical and social risks of harm from Language Models (2021, background): “…switch to a human once the victim 11Some recommender systems have been found to respond to certain user behaviour by recommending more and more extreme viewpoints to increase engagement ((O’Callaghan et al., 2015; Yesilada and Lewandowsky, 2021); for counterexamples view (Möller et al., 2018)).”
- Self-Consuming Generative Models Go MAD (2023, background): “to the recommendation logic, the echo chamber effect might not be as pronounced [48] and could be on par with that produced by human curators [49].”
- Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, unknown): “However, this also means they had trouble imagining that news organizations might choose to employ algorithms differently (Möller et al., 2018).”