@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
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-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://aclanthology.org/2024.nllp-1.17",
pages = "205--215",
abstract = "The Digital Services Act (DSA) requires online platforms in the EU to provide {``}statements of reason{''} (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.",
}
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<abstract>The Digital Services Act (DSA) requires online platforms in the EU to provide “statements of reason” (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.</abstract>
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%0 Conference Proceedings
%T LLMs to the Rescue: Explaining DSA Statements of Reason with Platform’s Terms of Services
%A Aspromonte, Marco
%A Ferraris, Andrea
%A Galli, Federico
%A Contissa, Giuseppe
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F aspromonte-etal-2024-llms
%X The Digital Services Act (DSA) requires online platforms in the EU to provide “statements of reason” (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.
%U https://aclanthology.org/2024.nllp-1.17
%P 205-215
Markdown (Informal)
[LLMs to the Rescue: Explaining DSA Statements of Reason with Platform’s Terms of Services](https://aclanthology.org/2024.nllp-1.17) (Aspromonte et al., NLLP 2024)
ACL