News and news recommender systems play a central role in shaping how people understand and interpret the world. Recent advances in generative AI have opened up new possibilities for content creation, personalization, multimodal analysis and interaction. While these technologies enhance automation and efficiency in news production and recommendation, they also introduce risks such as inconsistencies, misinformation, opinion polarization, and declining user trust. At the same time, the growing technical complexity of these methods, together with diverging regulatory requirements for fairness, transparency, and accountability in AI systems, poses significant challenge for news recommendation. The 14th International Workshop on News Recommendation and Analytics (INRA) serves as a dedicated venue for researchers and practitioners from various disciplines to share insights and explore how human-centered approaches can address this challenge by providing fair, transparent, sustainable and user-respecting news recommendations.

In today’s digital landscape, recommender systems assist users in navigating the vast amount of available data. Within the realm of information access, a subset of such systems called News Recommender Systems help users find news content that interests them. However, by prioritizing traditional accuracy-focused optimization, these systems contribute to the formation of filter bubbles, restricting users’ exposure to diverse viewpoints and exacerbating polarization. To address this issue, beyond-accuracy factors like diversity have been integrated into recommendation. Yet, such approaches can be ineffective and even unintentionally influence user opinions. This raises major ethical concerns as systems lack the legitimacy to shape opinions. This article presents the ADF framework, a novel approach designed to optimize accuracy, diversity, and fairness simultaneously. Unlike conventional models that manage fairness in a tradeoff, ADF establishes fairness as a core constraint. The framework relies on an innovative fairness-constrained diversification strategy, ensuring that users are exposed to a broader range of opinions, without being oriented toward specific viewpoints. ADF is adaptable to various diversity metrics and provides personalized diversification, independent of the underlying recommendation algorithms. Through real-world benchmark datasets and multiple recommendation models, experimental evaluation confirmed that ADF limits the impact on accuracy, enhances diversity, and crucially upholds fairness.

Personalization has changed how we engage with news. While information has become better accessible, users struggle to find information in the vast amount of news and news commentary published on a daily basis. The INRA workshop provides a forum to researchers, practitioners, and interested parties to discuss recent trends concerning news personalization. This edition of INRA highlights a variety of topics including generative AI, fake news, and multi-modality. Generative AI facilitates creating content at a rapid pace. That includes misleading information that can further erode the trust in media organizations. Texts and still images have dominated the era of printed news. Now, news organizations publish their information also in the form of podcasts and videos.

Recommender Systems have played an important role in our daily lives for many years. However, it is only recently that their social impact has raised ethical issues and has thus been considered in the design of such systems. Particularly, News Recommender Systems (NRS) have a critical influence on individuals. NRS can provide overspecialized recommendations and enclose users into filter bubbles. Besides, NRS can influence users and make their original opinions diverge. Worse, they can orient users’ opinions towards more radical views. The literature has worked on these issues by leveraging diversity and fairness in the recommendation algorithms, but generally only one of these dimensions at a time. We propose to consider both diversity and fairness simultaneously to provide recommendations that are fair, diverse, and obviously accurate. To this end, we propose a novel recommendation framework, Accuracy-Diversity-Fairness (ADF), which considers that fairness is not at the expense of diversity. Concretely, fairness is approached as a constraint on diversity. Experiments highlight that constraining diversity by fairness remarkably contributes to providing recommendations 5 times more diverse than models of the literature, without any loss in accuracy.

International audience