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  1.  47
    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.  1
    Detecting and explaining unfairness in consumer contracts through memory networks.Federico Ruggeri, Francesca Lagioia, Marco Lippi & Paolo Torroni - 2022 - 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. 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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  4. Type Extension Trees for Feature Construction and Learning in Relational Domains.Manfred Jaeger, Marco Lippi, Andrea Passerini & Paolo Frasconi - 2013 - Artificial Intelligence 204:30-55.
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  5.  5
    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.
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