Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives

Simon Jarvers, Chiara Ullstein, Jens Grossklags
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:281-287, 2025.

Abstract

The European Union’s AI regulation, the EU AI Act, represents a significant shift from voluntary ethical frameworks to binding regulation, presenting implementation challenges particularly for resource-limited SMEs. Our mixed-methods research examined the EU AI Act’s impact on SMEs through surveys of German AI SMEs (N=21) and interviews with AI SMEs and industry stakeholders (N=13). In this extended abstract, we summarize our motivation and methods, and focus on providing results from the interviews. Our findings reveal that company size and compliance experience significantly affect estimated implementation capabilities. SMEs face considerable resource constraints across time, finances, and staffing. Implementation uncertainties - including definitional ambiguity, unclear scope, and insufficient guidance - drive strategic responses: delaying compliance efforts, modifying products to reduce regulatory burden, and frequently seeking external compliance expertise and certification. These results indicate that uncertainty emerges as the primary implementation barrier. Researchers can help reduce uncertainty by developing best-practice guidelines that support the AI Act’s trustworthy AI objectives. We conclude with recommendations for policymakers and researchers.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-jarvers25a, title = {Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives}, author = {Jarvers, Simon and Ullstein, Chiara and Grossklags, Jens}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {281--287}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/jarvers25a/jarvers25a.pdf}, url = {https://proceedings.mlr.press/v294/jarvers25a.html}, abstract = {The European Union’s AI regulation, the EU AI Act, represents a significant shift from voluntary ethical frameworks to binding regulation, presenting implementation challenges particularly for resource-limited SMEs. Our mixed-methods research examined the EU AI Act’s impact on SMEs through surveys of German AI SMEs (N=21) and interviews with AI SMEs and industry stakeholders (N=13). In this extended abstract, we summarize our motivation and methods, and focus on providing results from the interviews. Our findings reveal that company size and compliance experience significantly affect estimated implementation capabilities. SMEs face considerable resource constraints across time, finances, and staffing. Implementation uncertainties - including definitional ambiguity, unclear scope, and insufficient guidance - drive strategic responses: delaying compliance efforts, modifying products to reduce regulatory burden, and frequently seeking external compliance expertise and certification. These results indicate that uncertainty emerges as the primary implementation barrier. Researchers can help reduce uncertainty by developing best-practice guidelines that support the AI Act’s trustworthy AI objectives. We conclude with recommendations for policymakers and researchers.} }
Endnote
%0 Conference Paper %T Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives %A Simon Jarvers %A Chiara Ullstein %A Jens Grossklags %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-jarvers25a %I PMLR %P 281--287 %U https://proceedings.mlr.press/v294/jarvers25a.html %V 294 %X The European Union’s AI regulation, the EU AI Act, represents a significant shift from voluntary ethical frameworks to binding regulation, presenting implementation challenges particularly for resource-limited SMEs. Our mixed-methods research examined the EU AI Act’s impact on SMEs through surveys of German AI SMEs (N=21) and interviews with AI SMEs and industry stakeholders (N=13). In this extended abstract, we summarize our motivation and methods, and focus on providing results from the interviews. Our findings reveal that company size and compliance experience significantly affect estimated implementation capabilities. SMEs face considerable resource constraints across time, finances, and staffing. Implementation uncertainties - including definitional ambiguity, unclear scope, and insufficient guidance - drive strategic responses: delaying compliance efforts, modifying products to reduce regulatory burden, and frequently seeking external compliance expertise and certification. These results indicate that uncertainty emerges as the primary implementation barrier. Researchers can help reduce uncertainty by developing best-practice guidelines that support the AI Act’s trustworthy AI objectives. We conclude with recommendations for policymakers and researchers.
APA
Jarvers, S., Ullstein, C. & Grossklags, J.. (2025). Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:281-287 Available from https://proceedings.mlr.press/v294/jarvers25a.html.

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