Equitative Algorithms for Legal Conflict Resolution

Amato, Alessandra; Amato, Flora; Cozzolino, Giovanni; Giacalone, Marco

doi:10.1007/978-3-030-33509-0_55

Cited by 1 publication

(1 citation statement)

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“…The project will involve a wide range of EU stakeholders as the primary target groups, as well as lawyers, notaries, consumer associations, academics, students and policymakers. The CREA2 project builds on its predecessor's results, CREA [8] (2017-2019). The general project aims to introduce artificial intelligence (AI)-driven tools to assist lay and legal people in resolving their disputes by applying game-theoretical (GT) algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Conversational Agent for the Legal Domain

et al. 2023

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An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it.

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Section: Introductionmentioning

confidence: 99%