There must be human-made objects that are
able to do most of the things humans do; especially the things that
require "intelligence."
"Can machines think?" Let's expand this question asked by Alan Turing in
the 50s. The countless disaster scenarios, in which artificial
intelligence (AI) takes over the world and destroys humanity, are
already made-up and still being told in Hollywood.
AI has not yet taken control of humanity, but it has indeed
taken control of many aspects of our lives even if we do not perceive it
as such. We accept AI as a part of our lives. The simplest example is
our smartphones!
The use of AI applications in this domain is so widespread
that it is now possible to produce solutions for almost all professional
groups. Medicine, education, automotive, defense, agriculture,
automation, energy, natural sciences, finance, art, and law!
Over the past 7 years, the sub-area of AI is deep learning.
Deep learning is more successful than humans especially in processing
visual data and analyzing images from the images, what objects or living
things exist, relationships with each other, event estimation,
object/person tracking, etc.
Deep learning includes AI models that generate the most
successful results in the application areas of recent years, based on
artificial neural networks and requiring a lot of processing power.
How do AI Systems Learn Language?
Models used for natural language processing are also within the scope of deep learning.
Using natural language processing models, we can parse
millions of data files loaded into the computer by class. In this
process, the system learns the relationship between words from all the
documents and is able to predict that the word 'carrot' comes after the
word 'rabbit' with higher probability than the word 'sun'.
AI can estimate this due to the fact that the words perform
meaning analysis based on their statistical status in sentences. It is
possible to summarize or classify a long paragraph, including time-space
information from the single sentences.
AI to Form Meaning Networks
More and more data is accumulating on the Internet every
day. Thanks to the enormous data, we can realize artificial intelligence
applications that are self-generating 'meaning networks'.
It not only informs us about the sociological,
psychological, ethnic, sociocultural and economic levels of communities
or people living in a region but also helps us predict where the
developing hot agenda can be, just like in the U.S. presidential
election.
Using the voice doctor application on our phone, we can try
to determine what our discomfort is and to perform pre-interventions
with very high accuracy. Babylon, which has more than 40,000 users in
the UK, is an exemplary AI assistant physician practice.
What if AI tries to practice as a lawyer?
Imagine that a 'human lawyer' can handle all the cases in the world after AI's preliminary research.
For a human lawyer, it takes weeks to do research, but AI
can do it in just a few seconds. Moreover, AI does not get tired, sleep,
eat or drink coffee. In fact, the AI can produce more successful
results than an average experienced lawyer.
What would you say to that? Will all the AI, machine learning from all these data eradicate the need for lawyers?
Leibniz: The First Lawyer to Predict the Use of Machines in Law
Leibniz,
who is one of the grandfathers of AI, was a lawyer and said: ‘It is
unworthy of excellent men to lose hours like slaves in the labor of
calculation which could safely be relegated to anyone else if machines
were used.'
In 1673, he presented the machine for four arithmetic
operations in the UK. Leibniz says 'The only way to correct our
reasoning is to make them as tangible as the mathematicians' so that we
can find our error at a glance, and when there are disagreements between
people, let's calculate and see who is right!'
So, let's think, why shouldn't it be possible for machines
to complete all steps of the event chain which occurs in a lawyer's mind
while they are deciding?
Why couldn't the machine do it? Why can it not calculate
who is right in the dispute between people or how to find the middle
way? Isn't that a 'robot mediator'? These questions belong to the 17th
century! I would like to point out, and we are at the end of 2018!
AINOW's Contribution to Create the Future of Legal AI
‘Since men near monopoly of all higher forms of
intelligence have been one of the most basic facts of human existence
throughout the past history of this planet, such developments would
clearly create a new economics, a new sociology, and a new history!'
says J. Schwartz.
In June 2018, AINOW—a
research institute examining the social implications of AI—convened a
workshop with the goal of bringing together legal, scientific, and
technical advocates who focus on litigating algorithmic decision-making across various areas of the law (e.g., employment, public benefits, criminal justice).
They structured the day with the practical aim of
discussing strategy and best practices while also exchanging ideas and
experiences in litigation and other advocacy in this space.
The gathering included several of the lawyers who brought
the cases alongside advocates, researchers, technical experts, social
scientists, and other leading thinkers in the area of algorithmic
accountability.
What is the accuracy rates of the AI Programs?
In 2017, in an experiment involving more than 100 lawyers
in London, hundreds of actual applications to the Finance Ombudsman for a
specific credit card irregularity were examined.
While the accuracy of human prediction was 66.3%, an AI
program trained to predict whether or not to accept files achieved 86.6%
accuracy.
Evisort to reduce the time cost of the Judicial System
Also, in late 2017, four Harvard Law School students argued that using AI to formulate and manage drafts of law contracts was a very accurate move.
With their powerful new search engine called Evisort that
harnesses cloud storage and AI, they hope to revolutionize the costly
and labor-intensive way that lawyers currently handle contracts and
other transactional work, liberating them for more creative and
interesting tasks.
When they say, "In six seconds they can review a 30-page
contract and pull out information for you", lawyers say, "Why did I
spend 10 years of my life doing that?" That reaction is what gets them
excited to keep going.
The way to reduce costs without sacrificing performance and
accuracy is through the use of deep learning and machine learning
models in natural language processing for law correspondence.
Performance Comparison of the AI Lawyers and the Human Lawyers
Another current study was from LawGeex,
which was founded in 2014. They compared the performance of 20
experienced United Nations lawyers to their AI systems and published a
40-page report.
Obtained results: In the daily legal risk assessment task,
the highest performance among human lawyers was 94%, the lowest
performance was 64%, and the average performance was 85%, while the
average of AI was 94% success.
In addition, the average time required for 'human lawyers'
for this process is 92 minutes, while the time needed by AI is 26
seconds. AI can continue this process for a long time without rest!
Historical Records are Helping the AI Systems
The most important part of the success of these studies is
that the data is regularly found in digital media. Harvard Law School
recently shared the 360-year-old case law of the United States of
America according to each of the states with AI developers on the online platform.
This is an important resource for speeding up the work. But
the biggest obstacles for natural language processing are
language-specific rules and the need for such resources in all
languages. The developments in English are quite bright because most of
the data is regularly available in this language.
Human- AI Lawyer Cooperation
As I mentioned at the beginning, these AI applications
which are developed by using data make similar inferences by looking at
the millions of cases in the past while giving a higher performance
compared to the success of a group of human lawyers.
In all these AI examples, human lawyers regained their lost
time. Artificial intelligence enables the human lawyer to work speed
and more data. These AI systems show us cooperation of human and AI is
important. That defined human-in-loop. It aims at providing lawyers more
consultancy and getting rid of fatigue duty.
For many applications of AI, the human-centered approach
supports the idea of human-AI collaboration instead of AI competing
against a human.
But what about the subjective observations and prejudices
that are stored in the data used by these machines? If the motives for
data transfer are not healthy, would it not make the decisions wrong? Is
it possible for AI to collect parser, connective, formative,
regenerator and operator which are included by ‘human lawyer'?
Systems could adopt the prejudices of the case documents so
far, such as those that were decided according to racist approaches in
the past. So AI systems in their current status are not prone to the
social conflicts that their context of creation contains within.
How do we provide serious judgment, attention, insightful
behavior, assessment, and judicial data to create a healthy judgment?
Perhaps the machine actually needs to be exposed with the past data or
to be manipulated by their human collaborators to overcome these issues.
Questions on AI Ethics
Is manipulation meant to interfere with the evolution of
the machine and is it wrong? We're coming to a point where AI ethics is
on the agenda!
Will the attorney receive support from the AI machine while
taking care of his job? Will AI software have their own rights? Can AI
machines participate in civil law or criminal law transactions?
When we give the same case to the machines where the same
programs are installed and when we want each machine to solve the same
problem more than once, will there be nuance differences between these
solutions? Should it be? According to time and space, how should we
differentiate each case?
Well, is 'human' considered to be 'robotized' in
professional execution today? Maybe we cannot remove prejudices from
human beings, but we need to keep people away from AI to prevent
prejudice. These are the issues that need to be discussed for a while.
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