# Unfair TOS: An Automated Approach using Customized BERT

arXiv: https://arxiv.org/abs/2401.11207 · HTML: https://mlsanity.com/paper/2401.11207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Revealing the Dark Secrets of BERT](https://mlsanity.com/paper/1908.08593/) (2019)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning](https://mlsanity.com/paper/1802.02561/) (2018)
- [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://mlsanity.com/paper/2003.10555/) (2020)
- [Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer](https://mlsanity.com/paper/2211.00974/) (2022)
- [Revisiting Token Dropping Strategy in Efficient BERT Pretraining](https://mlsanity.com/paper/2305.15273/) (2023)
- [Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers](https://mlsanity.com/paper/2211.15556/) (2022)

## Built on by (most cited first, 3 of 3)

- [Predicting potentially abusive clauses in Chilean terms of services with natural language processing](https://mlsanity.com/paper/2502.00865/) (2025)
- [AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation](https://mlsanity.com/paper/2505.14015/) (2025)
- [Text to Trust: Evaluating Fine-Tuning and LoRA Trade-offs in Language Models for Unfair Terms of Service Detection](https://mlsanity.com/paper/2510.22531/) (2025)

## Abstract (arXiv)

Terms of Service (ToS) form an integral part of any agreement as it defines the legal relationship between a service provider and an end-user. Not only do they establish and delineate reciprocal rights and responsibilities, but they also provide users with information on essential aspects of contracts that pertain to the use of digital spaces. These aspects include a wide range of topics, including limitation of liability, data protection, etc. Users tend to accept the ToS without going through it before using any application or service. Such ignorance puts them in a potentially weaker situation in case any action is required. Existing methodologies for the detection or classification of unfair clauses are however obsolete and show modest performance. In this research paper, we present SOTA(State of The Art) results on unfair clause detection from ToS documents based on unprecedented custom BERT Fine-tuning in conjunction with SVC(Support Vector Classifier). The study shows proficient performance with a macro F1-score of 0.922 at unfair clause detection, and superior performance is also shown in the classification of unfair clauses by each tag. Further, a comparative analysis is performed by answering research questions on the Transformer models utilized. In order to further research and experimentation the code and results are made available on https://github.com/batking24/Unfair-TOS-An-Automated-Approach-based-on-Fine-tuning-BERT-in-conjunction-with-ML.