A collaboration between Maastricht University, RTL and the University of Amsterdam.

Science Park 900, 1098 XH Amsterdam

The TAIM lab brings together two of the strongest groups on personalization and recommender systems in the Netherlands, the University of Amsterdam and the University of Maastricht, and a leading media organization, RTL, to develop trustworthy and personalized media.

With the largest commercial networks in the Netherlands, RTL plays an important role in society. They reach over 85% of Dutch people on a weekly basis, spending on average around 45 minutes a day with them and their content. RTL uses AI all across the value chain, from production to distribution, from automatically identifying interesting promotional material to providing a personalized content experience. At all these stages, we want to be able to trust our AI to be inclusive and steer away from unwanted bias. In particular, TAIM Lab focuses on developing AI that is reliable: RTL represents everyone in NL, that is their AI methods should not have a bias toward or against any group. Therefore, we adopt an intersectional approach to bias with regard to gender, age, background, etc., and optimize AI for diversity and inclusion. The research in this lab entails both ensuring diversity of voices (plurality) being expressed in the media, as well as a fair exposure to content for different groups of users. It is also essential to understand why some traditionally marginalized social groups distrust AI, and what can be done to develop trust, through the development of transparent systems in which the perspectives and needs of such groups are incorporated.

Throughout the TAIM Lab mixed methods are used: offline evaluation of open and internal data, as well as qualitative analysis (e.g., in structured interviews and panels). In this collaboration, we are able to jointly study fundamental issues of the long-term effects of AI in relation to fairness and inclusion. Having access to recommendation data and platforms allows us to study and measure fairness across different pipelines in a longitudinal manner which is rarely possible in academic projects. In fact, the findings of the majority of academic studies in recommender systems are difficult to translate to practice precisely for limited longitudinal data, relying many times on simulations and the assumptions made therein. At the same time, this allows us to overcome a frequent shortcoming in the industry where projects focus on short-term engagement metrics, potentially to the detriment of long-term metrics such as retention and conversion of new users.

Sustainable Development Goals

Research projects

Automated subtitling for TV. Aims to increase quality of automated subtitling for Dutch with TV-specific techniques.

Full page personalization. Aims to increase diversity of voices, and critically study the effect on engagement.

Synthetic media: Automatic promo material. Aims to understand the effects on bias, when automatically generating promos.

Perfect ad position. Aims to optimize fair advertising for consumers and advertisers in video-ondemand.

PhD5 –Diversity & bias in AI and content. Aims to recognize, assess, and mitigate bias, both algorithmic and in data, across the other PhD projects.


Maarten de Rijke
Nava Tintarev

PHD Students

Maarten de Rijke
Nava Tintarev


The stakeholders in RTL reach far beyond data science, including but not limited to: content operation teams responsible for subtitling, product owners and developers of the platform, promotional content creators, sellers of advertisements at AdAlliance, and product owners for the ad-based Videoland experience, and many crosscutting stakeholders on the topic of diversity and inclusion, lead by the communication team. Researchers from UM will be involved in the lab management and supervision of PhD students, as well as contributions from experienced faculty members. Maastricht contributes expertise in fairness in recommender systems, user-centered studies, and social science expertise in gender, diversity, and inclusion. Researchers from UvA will be involved in the lab management and supervision of PhD students, as well as contributions from experienced faculty members. Amsterdam contributes expertise in video analysis, recommender systems, language technologies, and search.


  • RTL

  • Maastricht University

  • University of Amsterdam
Maastricht University
University of Amsterdam

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