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.
Highlights
Artificial intelligence (AI)-based legal bots have attracted extensive consideration and have appeared as one of the most promising innovations of technology
To provide a framework for their behavior, we developed the following evaluation system, which refers to fourteen factors related to four aspects: attitude-related behaviors (ARB); perceived behavioral control (PBC); trust-related behaviors (TRB); and innovation resistance (IR)
Administrators need to focus on enhancing this aspect, followed by PBC A2, ARB A1, and TRB A3 sequentially, when evaluating the behavior of users and improving their implementation of legal AI bots
Summary
Artificial intelligence (AI)-based legal bots have attracted extensive consideration and have appeared as one of the most promising innovations of technology. The replacement of human labor, is becoming a crucial issue, as AI basically functions as an intelligence robot. The development of AI as a root for resolutions to various questions in life, including law, is getting more important. Experts or human workers are still needed to apply those legal expert systems. In 2017 an online AI platform, DoNotPay, which provides free legal advice, was released in the U.S It is called by Joshua Browder, its creator, the “first legal robot”, and it could deal with up to 1000 kinds of civil events [1]. The legal AI bot can help institutions that offer legal advice services, such as advice bureaus and community legal service centers for sustainable development
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