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Evaluating Large Language Models in a Complex Hidden Role Game A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development Query-Adaptive Semantic Chunking for Retrieval-Augmented Generation: A Dynamic Strategy with Contextual Window Expansion Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model How Far Will They Go? Red-Teaming Online Influence with Large Language Models RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation Learnability-Informed Fine-Tuning of Diffusion Language Models Graph Alignment Topology as an Inductive Bias for Grounding Detection Can AI Guess What You Know? 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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
Marie-Theres · 2026-05-25 · via cs updates on arXiv.org

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Abstract:Illegal content reporting mechanisms are a key technical and organizational measure through which online platforms address illegal content under the European Union Digital Services Act (DSA). Article 16 requires user notices to be sufficiently substantiated and submitted in good faith, placing users in the difficult position of interpreting legal and procedural language and translating ambiguous content into legally meaningful categories and reasons. We investigate how large language model (LLM)-based assistants can support this reporting process. In a controlled user study (N = 450) using an interface modeled on a major platform reporting workflow, we compare three conditions: unaided reporting, a conventional explainable AI assistant (XAI) that suggests a single legal category with a rationale, and an evaluative AI assistant (EvalAI) that presents balanced pro and con arguments across candidate legal provisions. We further examine these assistance forms under systematically varied AI error regimes. Our results show that EvalAI improves provision-level accuracy under AI error and reduces misclassification distance relative to conventional XAI, particularly for near-miss and overbreadth errors. When AI output is correct, conventional XAI enables faster decisions, but neither AI assistance form reliably improves the quality of users' substantiated explanations relative to unaided reporting. We discuss design implications for compliance-oriented reporting interfaces, highlighting trade-offs between accuracy, deliberation, explanation quality, and vulnerability to misleading AI output.
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.23676 [cs.HC]
  (or arXiv:2605.23676v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2605.23676

arXiv-issued DOI via DataCite (pending registration)
Related DOI: https://doi.org/10.1145/3805689.3812301

DOI(s) linking to related resources

Submission history

From: Shreyan Biswas [view email]
[v1] Fri, 22 May 2026 14:22:43 UTC (2,850 KB)

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