通过记忆网络检测和解释消费者合同中的不公平

IF 3.1 2区社会学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence and Law Pub Date : 2021-05-11 DOI:10.1007/s10506-021-09288-2

Federico Ruggeri, Francesca Lagioia, Marco Lippi, Paolo Torroni

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引用次数: 20

摘要

最近的工作表明，数据驱动的人工智能方法可以通过支持法律文件的自动分析来利用消费者保护。然而，数据驱动方法的一个缺点是解释性差。我们假设，在这个领域，可以通过诉诸法律依据来提供对分类器结果的有用解释。因此，我们考虑了记忆增强神经网络的几种配置，其中推理在上下文知识的建模中发挥了特殊作用。我们的结果表明，推理不仅有助于提高分类精度，而且能够对不透明的分类器结果提供有意义的、自然的语言解释。

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Detecting and explaining unfairness in consumer contracts through memory networks

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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来源期刊

Artificial Intelligence and Law Multiple-

CiteScore

9.50

自引率

26.80%

发文量

期刊介绍： Artificial Intelligence and Law is an international forum for the dissemination of original interdisciplinary research in the following areas: Theoretical or empirical studies in artificial intelligence (AI), cognitive psychology, jurisprudence, linguistics, or philosophy which address the development of formal or computational models of legal knowledge, reasoning, and decision making. In-depth studies of innovative artificial intelligence systems that are being used in the legal domain. Studies which address the legal, ethical and social implications of the field of Artificial Intelligence and Law. Topics of interest include, but are not limited to, the following: Computational models of legal reasoning and decision making; judgmental reasoning, adversarial reasoning, case-based reasoning, deontic reasoning, and normative reasoning. Formal representation of legal knowledge: deontic notions, normative modalities, rights, factors, values, rules. Jurisprudential theories of legal reasoning. Specialized logics for law. Psychological and linguistic studies concerning legal reasoning. Legal expert systems; statutory systems, legal practice systems, predictive systems, and normative systems. AI and law support for legislative drafting, judicial decision-making, and public administration. Intelligent processing of legal documents; conceptual retrieval of cases and statutes, automatic text understanding, intelligent document assembly systems, hypertext, and semantic markup of legal documents. Intelligent processing of legal information on the World Wide Web, legal ontologies, automated intelligent legal agents, electronic legal institutions, computational models of legal texts. Ramifications for AI and Law in e-Commerce, automatic contracting and negotiation, digital rights management, and automated dispute resolution. Ramifications for AI and Law in e-governance, e-government, e-Democracy, and knowledge-based systems supporting public services, public dialogue and mediation. Intelligent computer-assisted instructional systems in law or ethics. Evaluation and auditing techniques for legal AI systems. Systemic problems in the construction and delivery of legal AI systems. Impact of AI on the law and legal institutions. Ethical issues concerning legal AI systems. In addition to original research contributions, the Journal will include a Book Review section, a series of Technology Reports describing existing and emerging products, applications and technologies, and a Research Notes section of occasional essays posing interesting and timely research challenges for the field of Artificial Intelligence and Law. Financial support for the Journal of Artificial Intelligence and Law is provided by the University of Pittsburgh School of Law.