Media, news & democracy
Responsible news recommendation, AI-supported journalism, misinformation, provenance, usable transparency, selective exposure and news avoidance.
Interdisciplinary research initiative
Norwegian Computational Behaviour & AI Lab
Understanding how AI shapes human behaviour—and designing systems that support better choices, trustworthy information and societal value.
Behaviour + AIHuman-centred, empirical, responsible
Media & democracyTrust, provenance and recommendation
Health & foodAdvice, choice and behaviour change
Digital platformsPersonalisation, agency and long-term effects
The Norwegian Computational Behaviour & AI Lab is an interdisciplinary research initiative led by Professor Christoph Trattner at the University of Bergen. It connects researchers, students and external partners studying how recommender systems, generative AI and digital platforms influence what people see, choose, believe and trust.
Our work combines computational modelling, behavioural theory, human-centred AI and empirical evaluation. Rather than treating AI as a purely technical optimisation problem, we examine how systems interact with human agency, values, institutions and social contexts—particularly in media and democracy, health and food, and digital platforms.
Research pillars
Responsible AI grounded in human behaviour
Across application domains, the central question is the same: how can AI systems be useful and innovative without undermining agency, appropriate trust, diversity or societal values?
Health, food & behaviour
Food and health recommender systems, digital nudging, cross-cultural dietary behaviour, sustainability and the quality of AI-generated advice.
Recommenders & digital platforms
Personalisation, popularity bias, long-term user experience, platform behaviour and human–AI interaction beyond short-term engagement metrics.
Methods
From computational models to real-world evaluation
The initiative combines technical development with behavioural and societal evaluation.
Computational modelling
Recommender systems, machine learning, simulations and data-driven models of user behaviour.
User studies & experiments
Controlled studies, online experiments and longitudinal evaluations of behaviour, trust and choice.
Human-centred prototypes
Interfaces, explanations and decision-support tools designed around users and professional practices.
Partner-based field research
Living-lab studies and evaluations with media, technology, health and public-sector organisations.
Current directions
Questions shaping the research agenda
Appropriate trust, provenance and usable transparency
How can people recognise trustworthy AI-supported content without either rejecting AI blindly or relying on it uncritically?
Making important but low-interest content engaging
How can recommender systems broaden attention and curiosity instead of simply reinforcing existing interests?
Human oversight in AI-assisted journalism
How should AI support editors and journalists while preserving professional judgement, accountability and control?
Responsible AI for food and health decisions
How can personalised systems encourage healthier and more sustainable choices while avoiding manipulation and over-reliance?
Research environment
How the initiative connects
The Lab provides a thematic home across complementary research and innovation environments.
DARS research group →
Foundational and applied research on recommender systems, personalisation and user modelling.
SFI MediaFutures →
Large-scale research–industry collaboration and real-world experimentation in responsible media technology.
Interdisciplinary projects →
Collaborations across information science, media studies, psychology, health research, HCI and data science.
Selected scholarship
Flagship publications
A balanced selection representing the initiative’s work across responsible media AI, recommender systems and computational health and food behaviour.
Responsible AI, media & democracy
C2PA Provenance Labels Increase Trust in News Platforms Across Western Countries
Cross-country evidence on how content provenance affects trust in news platforms.
Increasing Editor Trust in News Personalization Systems with Fact-checked Large Language Models
Examines how evidence-grounded explanations can support editorial oversight of personalisation.
Leveraging Professional Ethics for Responsible AI: Applying AI Techniques to Journalism
Shows how professional journalism ethics can become practical design constraints for responsible AI.
Responsible Media Technology and AI: Challenges and Research Directions
A research agenda for responsible AI across news production, recommendation and media use.
Recommender systems & human behaviour
Examining the Merits of Feature-Specific Similarity Functions in the News Domain Using Human Judgments
Connects algorithmic similarity measures with how people actually judge related news content.
A Survey on Popularity Bias in Recommender Systems
Systematises how popularity bias emerges, how it affects stakeholders and how it can be evaluated and mitigated.
Health, food & behavioural AI
Supporting Healthier Food Choices Through AI-Tailored Advice: A Research Agenda
Outlines a human-centred agenda for personalised nutritional advice and responsible behaviour change.
Healthiness and Environmental Impact of Dinner Recipes Vary Widely Across Developed Countries
Large-scale evidence on nutritional quality and environmental impact across national recipe cultures.
Collaboration and supervision
The initiative welcomes research collaboration with academic, industry and public-sector partners, as well as strong student projects aligned with its research themes.