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Doing better with AI is not the same as retaining the skills needed to check its answers. An argument for keeping the human part of the thinking visible.
I lead research and innovation at the intersection of recommender systems, computational user behaviour and trustworthy AI — turning rigorous evidence into technologies, organisations and real societal impact.

Full Professor at the University of Bergen and Founder & Director of SFI MediaFutures
Research in the public conversation
New perspectives on AI, human judgement and trust, alongside research and events.
Doing better with AI is not the same as retaining the skills needed to check its answers. An argument for keeping the human part of the thinking visible.
Journalists need more than an “AI expert” label. I argue for naming the work behind an expert’s claims and making the limits of that expertise explicit.
Today’s students will teach tomorrow’s professionals. Schools need to assess both what learners produce with AI and what they can understand and do on their own.
An opinion piece on AI-generated health advice and the importance of discussing chatbot use in clinical consultations, drawing on research into the reliability of AI-generated nutrition information.
A changed news feed can change clicks without changing interests. Responsible recommendation should help readers discover more while respecting their decision to say no.
Content credentials document where an image came from. That is not the same as proving a news source deserves our trust. The distinction matters for readers and newsrooms.
VITAL is a prototype designed to help people assess online health claims, examine supporting evidence and follow transparent sources.
Read the announcementA four-wave study of news recommendation examines how article-selection behaviour relates to people’s stated topic profiles.
ACM DOIPDFAn argument for aligning researchers’ risk, control and rewards when universities seek to turn research into new companies.
The Arendalsuka 2026 programme included a MediaFutures session on responsible AI in Norwegian news media, with VITAL, verification and misinformation among the topics.
VITAL is a prototype designed to help people assess online health claims, examine supporting evidence and follow transparent sources.
Promising research can lose momentum between a project, a prototype and everyday use. Shared ownership and sustained collaboration need to survive those transitions.
Labelling synthetic material is only part of the task. Journalism also needs ways to make authentic content traceable when it travels beyond a publisher’s own platform.
AI-assisted work can look excellent without revealing what its author understands. Assessment needs to distinguish polished output from independent capability.
Our Research & Practice Note “Exploring Revealed Behavior and Stated Profiles in Longitudinal News Recommendation,” with Thomas Elmar Kolb and Alain Dominique Starke, reports a four-wave study with 262 participants. Preliminary results show that article-selection behavior shifts with feed composition while stated topic profiles remain comparatively stable—an important caution against treating short-term clicks or likes as stable preference change.
Universities prepare academics to teach. Leadership also needs preparation before people take responsibility for staff, budgets and institutional direction.
Our paper “Reducing Perceived Polarization through Affect-Balanced News Reframing,” with Jia-Hua Jeng, Alain D. Starke, David Elsweiler and Christoph Trattner, has been accepted for the ACM Conference on Recommender Systems 2026.
Students, researchers and lasting institutional knowledge are part of the infrastructure that turns research into value. Innovation policy should not separate them from the research it funds.
At ACM UMAP 2026 in Gothenburg, our team presented three papers spanning trustworthy news personalisation, selective news avoidance and AI-generated recipes. Tobias Wessel presented work on fact-checked LLMs for editor trust, Svenja Lys Forstner presented INRA work on explanations for low-interest news, and Yelyzaveta Lysova presented work on how users overlook nutritional flaws in LLM-generated recipes.
Our SFI MediaFutures paper on why explanations for low-interest news fail to persuade selective news avoiders is now available in the ACM UMAP 2026 workshop proceedings.
Two Research Council of Norway mobility projects connected to SFI MediaFutures and UiB were awarded, strengthening work on AI transparency, media literacy, vaccine beliefs and trustworthy health communication.
A quasi-experimental feasibility study shows how recommender-based support and structured documentation can contribute to operator performance in medical communication centres.
New work examines editor trust in fact-checked LLM-supported news personalisation and how users overlook nutritional flaws in LLM-generated recipes.
NoSoCSS is a new interdisciplinary initiative advancing computational social science research at the intersection of data, AI, platforms and society.
What I do
My work connects rigorous behavioural research with responsible technology design, institutional leadership and practical implementation.
Study how recommendation, personalization and generative AI shape choices, trust and information environments.
Explore research →Build interdisciplinary teams, partnerships and long-term programmes that move ideas from evidence to field deployment.
See selected impact →Help leaders, product teams and public organisations turn complex AI questions into clear strategy and responsible decisions.
View services →Selected impact
As founder and director of SFI MediaFutures, I conceived and built a national research–industry ecosystem for responsible media technology. The centre’s outputs are collective achievements. My contribution combines direct research and scientific leadership with the vision, consortium-building, funding, agenda-setting and organisational infrastructure that enable researchers and partners to translate work into tools, pilots and practice.
Partner outcomes
Selected research-to-practice and partner-outcome cases documented through MediaFutures, from provenance and verification to recommendation systems tested with partner data and users.

A three-country study translated content credentials into evidence for newsroom and platform design.

Tools developed for conflict verification were used by fact-checkers and adopted by teams beyond Norway.

Responsible personalisation moved from models and prototypes into partner pipelines and live-platform evaluation.
Profile
Christoph Trattner is one of Europe’s leading experts and a research and innovation leader in responsible recommender systems, computational user behaviour, and trustworthy AI. With more than 20 years of experience at the intersection of academia, industry, and applied AI innovation, he helps organisations design, evaluate, and govern AI systems that influence human decision-making in high-impact domains such as media, health, food, and consumer behaviour.
He is a Full Professor at the University of Bergen, Director of the Research Centre for Responsible Media Technology & Innovation (SFI MediaFutures), and founder and leader of the DARS research group, Norway’s largest research group on recommender systems. He also leads the Norwegian Computational Behaviour & AI Lab, where interdisciplinary teams develop responsible AI solutions for real-world societal and business challenges. In addition, he is a founding member and board member of NoSoCSS, an interdisciplinary initiative advancing computational social science in Norway and beyond.
Trattner has initiated and led large-scale research and innovation collaborations involving startups, public-sector organisations, multinational companies, and policy stakeholders across Europe and the United States. He has also been invited by public authorities to contribute to dialogue on responsible AI and research–industry collaboration, including an invitation from the Norwegian government to participate in the UN Internet Governance Forum in 2025. As founder and director of SFI MediaFutures, he conceived and built a national research–industry ecosystem that translates responsible AI research into practical technologies for journalism, media production, recommendation, fact-checking, accessibility, and democratic resilience.
Work with me on a keynote, advisory engagement or research collaboration →
Scholarship
Selected recent studies spanning responsible media AI, user trust and health-related generative AI.

Across three studies, the paper examines whether fear–hope reframing can reduce perceived polarization and negative emotional responses while preserving engagement-related outcomes.

A study of how fact-checked LLM support can shape editor trust in personalised news systems.

Examining how people assess the nutritional quality of recipes produced by large language models.

A cross-country study of how content provenance labels influence trust in digital news platforms.