Is It Worth Using LLMs for Unfair Clause Detection in Terms of Service?

Unfair clause detection is an extremely useful AI application for consumer protection. Artificial intelligence has recently been successful in building systems capable to automatically detect unfair clauses in Terms of Service, and also to identify their unfairness categories. Since Large Language Models (LLMs) are nowadays bringing a revolution to the field of artificial intelligence, and in particular to natural language processing and understanding, in this paper we compare several different prompt strategies for LLMs with more traditional BERT-based fine-tuned models. Our extensive experimental evaluation aims to investigate whether it is worth using LLMs also for this challenging domain-specific task.

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Published
2025-06-16
DOI
https://doi.org/10.1145/3769126.3769218
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
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article

Is It Worth Using LLMs for Unfair Clause Detection in Terms of Service?

Andrea Galassi, Rūta Liepiņa, Marco Lippi, Francesca Lagioia et al.
Hate Speech and Cyberbullying Detection
article

Is It Worth Using LLMs for Unfair Clause Detection in Terms of Service?

Andrea Galassi, Rūta Liepiņa, Marco Lippi, Francesca Lagioia, Marco Panarelli, Przemysław Pałka, Giovanni Sartor
article en

Abstract

Unfair clause detection is an extremely useful AI application for consumer protection. Artificial intelligence has recently been successful in building systems capable to automatically detect unfair clauses in Terms of Service, and also to identify their unfairness categories. Since Large Language Models (LLMs) are nowadays bringing a revolution to the field of artificial intelligence, and in particular to natural language processing and understanding, in this paper we compare several different prompt strategies for LLMs with more traditional BERT-based fine-tuned models. Our extensive experimental evaluation aims to investigate whether it is worth using LLMs also for this challenging domain-specific task.

Jagiellonian University (PL), University of Florence (IT), European University Institute (IT), University of Bologna (IT)
Peace, Justice and strong institutions
Openalex Percentile: Top 17%
Hate Speech and Cyberbullying Detection
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