A support system for the detection of abusive clauses in B2C contracts

被引:1
作者
Dadas, Slawomir [1 ]
Kozlowski, Marek [1 ]
Poswiata, Rafal [1 ]
Perelkiewicz, Michal [1 ]
Bialas, Marcin [1 ]
Grebowiec, Malgorzata [1 ]
机构
[1] Natl Informat Proc Inst, Al Niepodleglosci 188b, Warsaw, Poland
关键词
B2C contracts; Natural language processing; Machine learning; Neural networks; TERMS;
D O I
10.1007/s10506-024-09408-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Many countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.
引用
收藏
页数:39
相关论文
共 70 条
  • [1] Survey of Text Mining Techniques Applied to Judicial Decisions Prediction
    Alcantara Francia, Olga Alejandra
    Nunez-del-Prado, Miguel
    Alatrista-Salas, Hugo
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (20):
  • [2] Efficient Prediction of Court Judgments Using an LSTM plus CNN Neural Network Model with an Optimal Feature Set
    Alghazzawi, Daniyal
    Bamasag, Omaimah
    Albeshri, Aiiad
    Sana, Iqra
    Ullah, Hayat
    Asghar, Muhammad Zubair
    [J]. MATHEMATICS, 2022, 10 (05)
  • [3] Effective deep learning approaches for summarization of legal texts
    Anand, Deepa
    Wagh, Rupali
    [J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2022, 34 (05) : 2141 - 2150
  • [4] Apache Tika, 2007, Apache Software Foundation
  • [5] Does Anyone Read the Fine Print? Consumer Attention to Standard-Form Contracts
    Bakos, Yannis
    Marotta-Wurgler, Florencia
    Trossen, David R.
    [J]. JOURNAL OF LEGAL STUDIES, 2014, 43 (01) : 1 - 35
  • [6] Bhattacharya Paheli, 2021, ICAIL '21: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, P22, DOI 10.1145/3462757.3466092
  • [7] Bhattacharya Paheli, 2019, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11437), P413, DOI 10.1007/978-3-030-15712-8_27
  • [8] Bowman S. R., 2015, P 2015 C EMPIRICAL M, P632, DOI DOI 10.18653/V1/D15
  • [9] Chalkidis Ilias, 2020, FIND ASS COMP LING
  • [10] Dadas Slawomir, 2020, Artificial Intelligence and Soft Computing. 19th International Conference, ICAISC 2020. Proceedings. Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science (LNAI 12416), P301, DOI 10.1007/978-3-030-61534-5_27