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.

Deep learning for detecting and explaining unfairness in consumer contracts / Lagioia, F.; Ruggeri, F.; Drazewski, K.; Lippi, M.; Micklitz, H. -W.; Torroni, P.; Sartor, G.. - 322(2019), pp. 43-52. ((Intervento presentato al convegno 32nd International Conference on Legal Knowledge and Information Systems, JURIX 2019 tenutosi a Artificial Intelligence Department of the Technical University of Madrid, esp nel 2019.

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