Detecting and explaining unfairness in consumer contracts through memory networks

Federico Ruggeri; Francesca Lagioia; Marco LippiPaolo Torroni

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Detecting and explaining unfairness in consumer contracts through memory networks

机译：Detecting and explaining unfairness in consumer contracts through memory networks

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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.

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《Artificial intelligence and law》 |2022年第1期|59-92|共34页
作者
Federico Ruggeri; Francesca Lagioia; Marco LippiPaolo Torroni;
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作者单位

DISI, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;

Law Department, European, CIRSFID-AI, University of Bologna, Bologna, Italy;

DISMI, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy;

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正文语种英语
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关键词
Unfair clause detection; Deep learning; Memory networks; Explainability; Legal rationales;

Detecting and explaining unfairness in consumer contracts through memory networks

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