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  1.  66
    CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service.Marco Lippi, Przemysław Pałka, Giuseppe Contissa, Francesca Lagioia, Hans-Wolfgang Micklitz, Giovanni Sartor & Paolo Torroni - 2019 - Artificial Intelligence and Law 27 (2):117-139.
    Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.
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  2.  28
    Detecting and explaining unfairness in consumer contracts through memory networks.Federico Ruggeri, Francesca Lagioia, Marco Lippi & Paolo Torroni - 2021 - Artificial Intelligence and Law 30 (1):59-92.
    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 (...)
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  3.  15
    Unfair clause detection in terms of service across multiple languages.Andrea Galassi, Francesca Lagioia, Agnieszka Jabłonowska & Marco Lippi - forthcoming - Artificial Intelligence and Law.
    Most of the existing natural language processing systems for legal texts are developed for the English language. Nevertheless, there are several application domains where multiple versions of the same documents are provided in different languages, especially inside the European Union. One notable example is given by Terms of Service (ToS). In this paper, we compare different approaches to the task of detecting potential unfair clauses in ToS across multiple languages. In particular, after developing an annotated corpus and a machine learning (...)
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  4.  28
    Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?Andrea De Cesarei, Shari Cavicchi, Giampaolo Cristadoro & Marco Lippi - 2021 - Cognitive Science 45 (6):e13009.
    The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear. Here, we investigated this issue by engaging humans and CNNs in a two‐class visual categorization task. To this end, pictures containing animals or vehicles were modified to contain (...)
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  5.  10
    Aggregation and the Microfoundations of Dynamic Macroeconomics.Mario Forni & Marco Lippi - 1997 - Oxford University Press UK.
    This book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. Standard models start with intertemporally maximizing agents and obtain dynamic equations linking economic variables like consumption, income, investment interest rate and employment. Such equations exhibit testable properties like cointegration, definite patterns of Granger causality, and restrictions on the parameters. The usual simplification that agents are identical leads to testing these properties directly on aggregate data. Here this simplification is systematically questioned. In Part I the homogeneity (...)
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  6.  10
    Type Extension Trees for feature construction and learning in relational domains.Manfred Jaeger, Marco Lippi, Andrea Passerini & Paolo Frasconi - 2013 - Artificial Intelligence 204 (C):30-55.
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