FAIR & AI Symposium @ TU Graz

This record contains the presentations of the the FAIR & AI Symposium, which took place on the 27th of November at TU Graz. The recordings are publicly available (see related works). FAIR & AI Symposium Highlights: A Community Shaping the Future of Trustworthy Research Data and AI The FAIR & AI Symposium gathered researchers, data stewards, infrastructure and policy experts to examine how FAIR research data and AI increasingly shape one another. Participants asked whether the FAIR principles remain sufficient as AI transforms data creation, management, and reuse. Sessions showed that AI can significantly support FAIR practices—through automated metadata, semantic enrichment, and better discoverability—while also raising challenges around transparency, privacy, bias, and accountability. Human oversight remains essential. Keynotes underscored growing needs for professional data management and persistent issues of data quality. Jana Lasser highlighted privacy risks and the demand for skilled data stewards; Daniel Garijo argued that FAIR is a means to improve credit, reproducibility, and trustworthy AI, but quality gaps persist. Lightning talks covered HPC access, AI governance, and the evolving role of data stewards. Breakout groups explored how AI can aid FAIRification and how FAIR data strengthens AI. Participants agreed that standardized tools, certification, and automated support for repetitive tasks will be crucial. An overview of the event program can be found in the file: Overview.pdfThe report of the event can be found in the file: Report.pdf Keynote Presentations Jana Lasser: FAIR data vs. GDPR - a researchers perspective (see: FAIR_vs_GDPR_Lasser.txt) Daniel Garijo: Beyond FAIR for data: Quality in Heterogeneous Digital Objects (see: Towards_FAIR_for_AI_Garijo) Lightning Talk Presentations Markus Stöhr: Infrastructure for HPC and AI workloads (see: Infrastructure_for_HPC_and_AI_workloads_in_Austria_and_Europe_Stöhr.pdf) Jeannette Gorzala: The Future runs on Trust Code (see: The_Future_runs_on_Trust_Code_Gorzala.pdf) Emily Kate & Michael Feichtinger: A Long Way to FAIR and a Short Time to Get There: Small Steps Toward Big Promises (see: A_Long_Way_to_FAIR_and_Short_Time_to_get_there_Kate_Feichtinger.pdf) Please check the license terms specified in the presentations for the reuse of the content. For more information, the event webpage is linked (see references).

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Publication Details

Journal
TUGraz OPEN Library (Graz University of Technology)
Published
2025-12-06
DOI
https://doi.org/10.3217/czym3-9hw53
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
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FAIR & AI Symposium @ TU Graz

TUGraz OPEN Library (Graz University of Technology)
Research Data Management Practices
article

FAIR & AI Symposium @ TU Graz

article en

Abstract

This record contains the presentations of the the FAIR & AI Symposium, which took place on the 27th of November at TU Graz. The recordings are publicly available (see related works). FAIR & AI Symposium Highlights: A Community Shaping the Future of Trustworthy Research Data and AI The FAIR & AI Symposium gathered researchers, data stewards, infrastructure and policy experts to examine how FAIR research data and AI increasingly shape one another. Participants asked whether the FAIR principles remain sufficient as AI transforms data creation, management, and reuse. Sessions showed that AI can significantly support FAIR practices—through automated metadata, semantic enrichment, and better discoverability—while also raising challenges around transparency, privacy, bias, and accountability. Human oversight remains essential. Keynotes underscored growing needs for professional data management and persistent issues of data quality. Jana Lasser highlighted privacy risks and the demand for skilled data stewards; Daniel Garijo argued that FAIR is a means to improve credit, reproducibility, and trustworthy AI, but quality gaps persist. Lightning talks covered HPC access, AI governance, and the evolving role of data stewards. Breakout groups explored how AI can aid FAIRification and how FAIR data strengthens AI. Participants agreed that standardized tools, certification, and automated support for repetitive tasks will be crucial. An overview of the event program can be found in the file: Overview.pdfThe report of the event can be found in the file: Report.pdf Keynote Presentations Jana Lasser: FAIR data vs. GDPR - a researchers perspective (see: FAIR_vs_GDPR_Lasser.txt) Daniel Garijo: Beyond FAIR for data: Quality in Heterogeneous Digital Objects (see: Towards_FAIR_for_AI_Garijo) Lightning Talk Presentations Markus Stöhr: Infrastructure for HPC and AI workloads (see: Infrastructure_for_HPC_and_AI_workloads_in_Austria_and_Europe_Stöhr.pdf) Jeannette Gorzala: The Future runs on Trust Code (see: The_Future_runs_on_Trust_Code_Gorzala.pdf) Emily Kate & Michael Feichtinger: A Long Way to FAIR and a Short Time to Get There: Small Steps Toward Big Promises (see: A_Long_Way_to_FAIR_and_Short_Time_to_get_there_Kate_Feichtinger.pdf) Please check the license terms specified in the presentations for the reuse of the content. For more information, the event webpage is linked (see references).

TUGraz OPEN Library (Graz University of Technology)
Graz University of Technology (AT)
Industry, innovation and infrastructure
Openalex Percentile: Top 29%
Research Data Management Practices
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