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FAIR & AI symposium

Experts debated whether the FAIR guiding principles are still enough for data stewardship as AI transforms how data is created, managed, and reused.

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© Helmut Lunghammer

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From left to right: Sotirios Tsepelakis, Suvini Lai, Barbara Sánchez Solís, Livia Beck, Florina Piroi and Christiane Stork from the TU Wien Center for Research Data Management

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© Helmut Lunghammer

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Moderators of the FAIR & AI Symposium: Suvini Lai and Livia Beck

The FAIR & AI Symposium, organised under Cluster Forschungsdaten, opens an external URL in a new window, brought together a community of researchers, infrastructure operators, data stewards, and policy experts to explore one of today’s most pressing intersections: the evolving relationship between FAIR research data and artificial intelligence (AI).

The event was organised by the TU Graz RDM team and held in the historic main building’s aula on 27 November 2025. It sparked lively discussions on how data management and AI can advance together responsibly and sustainably. A recurring theme was the complementarity of FAIR and AI, alongside persistent challenges around privacy, transparency, and bias. 

The sessions made clear that while FAIR remains a strong foundation, AI brings both powerful opportunities and new responsibilities. Automated metadata generation, semantic enrichment, and improved data discoverability were highlighted as significant enablers for FAIRification. At the same time, speakers emphasised that challenges such as transparency, bias detection, accountability, and ethical decision-making cannot be delegated to machines alone.

Keynotes

The participants engaged in a dynamic mix of keynote, opens an external URL in a new window addresses, expert presentations, lightning talks, and hands-on discussions. Jana Lasser (University of Graz) opened the symposium with “tales from the trenches” on handling sensitive data, presenting three case studies on the privacy challenges of FAIR data. She stressed that rapidly evolving requirements – such as anonymising unstructured data – demand expertise beyond individual researchers, increasing the need for professional Data Stewards and making secure data management a competitive advantage.

Daniel Garijo (Polytechnic University of Madrid) followed with a keynote on quality in heterogeneous digital objects. He argued that FAIR is a means to broader goals like scientific credit, dataset recognition, and reproducibility, noting that while FAIR is essential for AI, data quality remains an open challenge.

Lightning talks then covered:

  • Markus Stöhr on national and European HPC access;
  • Jeannette Gorzala on legal considerations for AI and data-driven research;
  • Emily Kate on the evolving role of data stewards and trustworthy AI.

Presentations and recordings

All event recordings and presentations can be accessed via the Cluster Forschungsdaten website, opens an external URL in a new window.

Collaboration in Action

The event was organised in a genuinely collaborative spirit. Although organised and hosted by TU Graz, contributions came from across Austria: the University of Graz delivered the opening keynote, the University of Vienna represented the topic of data stewardship, and TU Wien – through Sabine Neff-Kolassa (Research Information Systems), highlighted the role of Cluster Forschungsdaten in strengthening collaboration among Austrian universities. Livia Beck and Suvini Lai from the Center for Research Data Management moderated the entire event. Florina Piroi, another team member of the Center, introduced one of the two breakout discussions, addressing the question “How can FAIR data practices support AI applications?”

Contact

TU Wien
Center for Research Data Management
research.data@tuwien.ac.at