@inproceedings{aspromonte-etal-2024-llms,
    title = "{LLM}s to the Rescue: Explaining {DSA} Statements of Reason with Platform`s Terms of Services",
    author = "Aspromonte, Marco  and
      Ferraris, Andrea  and
      Galli, Federico  and
      Contissa, Giuseppe",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.nllp-1.17/",
    doi = "10.18653/v1/2024.nllp-1.17",
    pages = "205--215",
    abstract = "The Digital Services Act (DSA) requires online platforms in the EU to provide {\textquotedblleft}statements of reason{\textquotedblright} (SoRs) when restricting user content, but their effectiveness in ensuring transparency is still debated due to vague and complex terms of service (ToS). This paper explores the use of NLP techniques, specifically multi-agent systems based on large language models (LLMs), to clarify SoRs by linking them to relevant ToS sections. Analysing SoRs from platforms like Booking.com, Reddit, and LinkedIn, our findings show that LLMs can enhance the interpretability of content moderation decisions, improving user understanding and engagement with DSA requirements."
}