Exploring the implementation of a legal AI bot for sustainable development in legal advisory institutions

Abstract

This study explores the implementation of legal artificial intelligence (AI) robot issues for sustainable development related to legal advisory institutions. While a legal advisory AI Bot using the unique arithmetic method of AI offers rules of convenient legal definitions, it has not been established whether users are ready to use one at legal advisory institutions. This study applies the MCDM (multicriteria decision-making) model DEMATEL (decision-making trial and evaluation laboratory)-based Analytical Network Process (ANP) with a modified VIKOR, to explore user behavior on the implementation of a legal AI bot. We first apply DEMATEL-based ANP, called influence weightings of DANP (DEMATEL-based ANP), to set up the complex adoption strategies via systematics and then to employ an M-VIKOR method to determine how to reduce any performance gaps between the ideal values and the existing situation. Lastly, we conduct an empirical case to show the efficacy and usefulness of this recommended integrated MCDM model. The findings are useful for identifying the priorities to be considered in the implementation of a legal AI bot and the issues related to enhancing its implementation process. Moreover, this research offers an understanding of users' behaviors and their actual needs regarding a legal AI bot at legal advisory institutions. This research obtains the following results: (1) It effectively assembles a decision network of technical improvements and applications of a legal AI bot at legal advisory institutions and explains the feedbacks and interdependences of aspects/factors in real-life issues. (2) It describes how to vary effective results from the current alternative performances and situations into ideal values in order to fit the existing environments at legal advisory institutions with legal AI bot implementation.