The Future of Legal Artificial Intelligence (a.i.)—a Discourse on A.i. Components, Levels, and Biases

• Publication year: 2021
• Author: Michael Andrew Iseri

The Future of Legal Artificial Intelligence (A.I.)—A Discourse on A.i. Components, Levels, and Biases1

Michael Andrew Iseri

Michael Iseri is a Technology Law and Disability Rights Attorney, Cyber Security Professional, Legal Tech Accessibility Adviser, and Software Engineer. Driven by these unique skills, he wants to ensure that legal accessibility is available to all.

A. Introduction

The legal field—as with other professions—is undergoing a transformative phase that would integrate more advance technology into its legal services. Technology adoption rates have accelerated in 2020 due to COVID-19 restrictions for in-person meetings and legal hearings. Like at a trial with inadmissible evidence, the door is now wide-open to bring in technology.

This article serves as a primer on the current state of A.I. and its application to the legal field. To note, there are few resources that clearly define legal technologies—especially legal A.I.—without misleading marketing terms, grandiose claims/gimmicks, or incompatible real-world applications. Most importantly, there are different classes and case scenarios of A.I. existing in the real world such as search engine A.I., content generation A.I., navigation A.I. (such as self-driving cars), auto-response A.I., and much more. The knowledge in this article is based on the author's unique perspective as an attorney and software engineer.² The information has also been vetted through numerous dialogues with various software engineers from Google and Uber in San Francisco and Silicon Valley.³

To begin, this article would provide an overview of the three components of an A.I. to better equip the reader with the ability to understand and characterize the different A.I. programs out in the real world. Second, there will be a brief discussion on how the three A.I. components create different levels of A.I. complexities in the real world. Lastly, this article would provide an overview on the different types of biases that could "corrupt" an A.I. program during its development and implementation stages that would likely impact any development of a legal A.I.

B. The Three A.I. Components—(1) Human Interfaces, (2) Intelligent Automatization (IA), and (3) Machine Learning (ML)

In the programming world, there is no such thing as a "true A.I.," a program that codes itself to evolve and adapt. The stuff in Hollywood and films have tainted the populace's perceptions on what A.I. truly represents for numerous professions.

There are three main components of A.I. that can characterize an A.I. program in different professions. They are the following:

(1) Human Interface: This component is the main means of an A.I. program to communicate with humans, whether through sight, sound, touch (haptic feedback), or other means. Without this component, a program would not be able to receive or communicate back to humans on its operations. Main examples are dialogue boxes and webpages, a chatbot, voice interfaces such Amazon's Alexa and Apple's Siri, vibrations, sirens and alarms, and other means.
(2) Intelligent Automation Tools (IA): This component essentially defines an A.I. program's identity and core functions by establishing its tools and operations. IA tools are coded instructions that provide the necessary means for an A.I. program to do what it is programmed to do. It is analogous to the tools that a human would use to accomplish a unique goal, such as using a saw and hammer to build a table. Most importantly, these IA tools have been programmed by humans, and no A.I. programs have been able to truly build their own IA tools outside of a control environment. Currently, IA tools are the limiting factors for A.I. programs to evolve since they require humans to program new parameters and functions. For example, a human can easily play chess or game that include more extra rows and columns than a conventional gameboard; however, an A.I. program would not be able to understand these extra outside rules without being programmed to anticipate that possibility if it is not within its existing IA tools parameters.

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(3) Machine

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