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

Lagioia Francesca;Ruggeri Federico;Torroni Paolo;Sartor Giovanni
2019

Abstract

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.
Legal Knowledge and Information Systems. JURIX 2019: The Thirty-second Annual Conference
43
52
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
Lagioia Francesca; Ruggeri Federico; Drazewski Kasper; Lippi Marco; Micklitz Hans-Wolfgang; Torroni Paolo; Sartor Giovanni
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/716457
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