Can't LLMs Do That? Supporting Third-Party Audits Under the DSA: Exploring Large Language Models for Systemic Risk Evaluation of the Digital Services Act in an Interdisciplinary Setting

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Datum
2025
Herausgeber:innen
Autor:innen
Sekwenz, Marie-Therese
Gsenger, Rita
Stocker, Volker
Görnemann, Esther
Talypova, Dinara
Parkin, Simon
Greminger, Lea
Smaragdakis, Georgios
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Verlag
Association for Computing Machinery
Zusammenfassung

This paper investigates the feasibility and potential role of using Large Language Models (LLMs) to support systemic risk audits under the European Union’s Digital Services Act (DSA). It examines how automated tools can enhance the work of DSA auditors and other ecosystem actors by enabling scalable, explainable, and legally grounded content analysis. An interdisciplinary expert workshop with twelve participants from legal, technical, and social science backgrounds explored prompting strategies for LLM-assisted auditing. Thematic analysis of the sessions identified key challenges and design considerations, including prompt engineering, model interpretability, legal alignment, and user empowerment. Findings highlight the potential of LLMs to improve annotation workflows and expand audit scale, while underscoring the continued importance of human oversight, iterative testing, and cross-disciplinary collaboration. This study offers practical insights for integrating AI tools into auditing processes and contributes to emerging methodologies for operationalizing systemic risk evaluations under the DSA.

Beschreibung
Schlagwörter
Large Language Models \ Digital Services Act \ Online Platform Auditing \ Systemic Risk \ Content Moderation \ Human-AI Collaboration
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Zitierform
Sekwenz, M.-T., Gsenger, R., Stocker, V., Görnemann, E., Talypova, D., Parkin, S., Greminger, L., & Smaragdakis, G. (2025, Juni 22). Can’t LLMs Do That? Supporting Third-Party Audits Under the DSA: Exploring Large Language Models for Systemic Risk Evaluation of the Digital Services Act in an Interdisciplinary Setting. Adjunct proceedings of the 4th annual symposium on human-computer interaction for work. https://doi.org/10.1145/3707640.3731929