Detecting and explaining unfairness in consumer contracts through memory networks

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Abstract

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.

Citation

Federico Ruggeri, Francesca Lagioia, Marco Lippi, and Paolo Torroni. Detecting and explaining unfairness in consumer contracts through memory networks. Artif. Intell. Law, 30(1):59–92, 2022.

@article{ruggeri-etal-2022-detecting,
  author    = {Federico Ruggeri and
               Francesca Lagioia and
               Marco Lippi and
               Paolo Torroni},
  title     = {Detecting and explaining unfairness in consumer contracts through
               memory networks},
  journal   = {Artif. Intell. Law},
  volume    = {30},
  number    = {1},
  pages     = {59--92},
  year      = {2022},
  url       = {https://doi.org/10.1007/s10506-021-09288-2},
  doi       = {10.1007/s10506-021-09288-2},
  timestamp = {Sun, 02 Oct 2022 15:27:01 +0200},
  biburl    = {https://dblp.org/rec/journals/ail/RuggeriLLT22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
}