92 research outputs found
Explaining potentially unfair clauses to the consumer with the claudette tool
This paper presents the latest developments of the use of memory network models in detecting and explaining unfair terms in online consumer contracts. We extend the CLAUDETTE tool for the detection of potentially unfair clauses in online Terms of Service, by providing to the users the explanations of unfairness (legal rationales) for five different categories: Arbitration, unilateral change, content removal, unilateral termination, and limitation of liability
Deep Learning for Detecting and Explaining Unfairness in Consumer Contracts
Consumer contracts often contain unfair clauses, in apparent violation of the rel- evant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural net- works that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only pro- vide reasons and explanations to the user, but also enhance the automated detection of unfair clauses
Deep learning for detecting and explaining unfairness in consumer contracts
Consumer contracts often contain unfair clauses, in apparent violation of the relevant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural networks that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only provide reasons and explanations to the user, but also enhance the automated detection of unfair clauses
Detecting and Explaining Unfairness in Consumer Contracts with Memory Networks
open4noAn extended version of this paper has been submitted for publication in a peer-reviewed journal. In this paper we present recent advancements of a project named CLAUDETTE http://claudette.eui.eu/openFederico Ruggeri, Francesca Lagioia, Marco Lippi, Paolo TorroniFederico Ruggeri, Francesca Lagioia, Marco Lippi, Paolo Torron
Detecting and explaining unfairness in consumer contracts through memory networks
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes
The Force Awakens: Artificial Intelligence for Consumer Law
Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision
Detecting and explaining unfairness in consumer contracts through memory networks
Published online: 11 May 2021Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.
Sponsor information: Francesca Lagioia has been supported by the European Research Council (ERC) Project âCompuLawâ (Grant Agreement No. 833647) under the European Unionâs Horizon 2020 research and innovation programme. Paolo Torroni has been partially supported by the H2020 Project AI4EU (Grant Agreement No. 825619). Marco Lippi would like to thank NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research.Open access funding provided by Alma Mater Studiorum - UniversitĂ di Bologna within the CRUI-CARE Agreement
The Force Awakens: Artificial intelligence for consumer law
Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision
AI in search of unfairness in consumer contracts : the terms of service landscape
Published online: 18 July 2022This article explores the potential of artificial intelligence for identifying cases where digital vendors fail to comply with legal obligations, an endeavour that can generate insights about business practices. While heated regulatory debates about online platforms and AI are currently ongoing, we can look to existing horizontal norms, especially concerning the fairness of standard terms, which can serve as a benchmark against which to assess business-to-consumer practices in light of European Union law. We argue that such an assessment can to a certain extent be automated; we thus present an AI system for the automatic detection of unfair terms in business-to-consumer contracts, a system developed as part of the CLAUDETTE project. On the basis of the dataset prepared in this project, we lay out the landscape of contract terms used in different digital consumer markets and theorize their categories, with a focus on five categories of clauses concerning (i) the limitation of liability, (ii) unilateral changes to the contract and/or service, (iii) unilateral termination of the contract, (iv) content removal, and (v) arbitration. In so doing, the paper provides empirical support for the broader claim that AI systems for the automated analysis of textual documents can offer valuable insights into the practices of online vendors and can also provide valuable help in their legal qualification. We argue that the role of technology in protecting consumers in the digital economy is critical and not sufficiently reflected in EU legislative debates.Francesca Lagioia has been supported by the European Research Council (ERC) Project âCompuLawâ (Grant Agreement No 833647) under the European Unionâs Horizon 2020 research and innovation programme, and by the SCUDO project, within the POR-FESR 2014-2020 programme of Regione Toscana. Agnieszka JabĆonowska has been supported by the National Science Center in Poland (Grant Agreement UMO-2019/35/B/HS5/04444). This work has been supported by the Claudette (CLAUseDETecTEr) project, funded by the Research Council of the European University Institute and from the Bureau EuropĂ©en des Unions de Consommateurs (BEUC)
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