Balancing Needs and Values: A Multi-Stakeholder Examination of Algorithmic News Recommenders in the Netherlands
Bibliographic record
Abstract
This paper aims to deepen understanding of the negotiation process underlying the values embedded in algorithmic news recommenders. The focus is on examining the perceptions and aspirations of different stakeholders and what values are ultimately incorporated in the design of a recommender system. Specifically, it investigates the development of value-driven recommendations at a leading Dutch online news platform, employing a combination of aspects of participatory action research and a multi-stakeholder framework. This is achieved through workshops and interviews with practitioners, critically examining the constraints and value tradeoffs that emerge among key internal stakeholders: journalists, editors, chief editors, and the technical team. The paper reveals how there is a tendency to prioritize technical aspects that align with immediate business goals. This does not stem from ill-intent or an unwillingness to explore other values but has practical reasons. Additionally, the study uncovers reservations and misconceptions about recommender systems by certain stakeholders, highlighting the need for improved understanding and dialogue among stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".