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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dc.contributor.authorSekwenz, Marie-Therese
dc.contributor.authorGsenger, Rita
dc.contributor.authorStocker, Volker
dc.contributor.authorGörnemann, Esther
dc.contributor.authorTalypova, Dinara
dc.contributor.authorParkin, Simon
dc.contributor.authorGreminger, Lea
dc.contributor.authorSmaragdakis, Georgios
dc.date.accessioned2025-07-03T11:49:18Z
dc.date.available2025-07-03T11:49:18Z
dc.date.issued2025
dc.description.abstractThis 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.
dc.identifier.citationSekwenz, 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
dc.identifier.doi10.1145/3707640.3731929
dc.identifier.isbn979-8-4007-1397-2
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/925
dc.language.isoeng
dc.publisherAssociation for Computing Machinery
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLarge Language Models
dc.subjectDigital Services Act
dc.subjectOnline Platform Auditing
dc.subjectSystemic Risk
dc.subjectContent Moderation
dc.subjectHuman-AI Collaboration
dc.titleCan'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
dc.typeConferencePaper
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.urlhttps://doi.org/10.1145/3707640.3731929
local.researchgroupDigitale Ökonomie, Internet, Ökosystem und Internet-Policy
local.researchgroupNormsetzung und Entscheidungsverfahren
local.researchtopicDigitale Märkte und Öffentlichkeiten auf Plattformen
local.researchtopicDigitale Infrastrukturen in der Demokratie
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