1.
Systemising Advice Artificial Intelligence and
Legal Practice

2.
• What is AI
• Examples of Legal Expert Systems
• Advice as a Commodity
• AI and Litigation
• Possible Futures – Precedent or
Page Ranking

3.
What is AI

4.
• Technologies that seek to mimic
cognitive functions humans
typically associate with other
human minds, such as learning and
problem solving.

5.
Areas Where AI
Deployed
• Transportation logistics and
planning
• Financial services
• Law – document assembly –
LexisNexis and Westlaw

6.
Three Types
• Legal Expert
Systems
• Predictive Analytics
• Machine Learning

7.
Legal Expert Systems

8.
Applications or programs
that replicate the thinking
and actions of an expert on
a specific question or task.
They enable many people
to benefit from the
expertise and judgement of
experts anytime anywhere
and cost effectively.

9.
• Usually deployed where
there are case-based or
rule-based outcomes

10.
Rule Based
Store legal
knowledge as
rules
Data inputs lead
to an outcome
based on rules

11.
Case Based
•Operate by comparing the
intersections of facts in a database of
past cases, called exemplars, with the
facts in the present situation.
•The case-based system attempts to
draw analogies between the
exemplars and the present case in
order to retrieve the most on point
cases

12.
So How Do Legal Expert Systems
Work?

13.
Predictive
Analytics

14.
• Extracting information from existing
data sets to determine patterns and
predict future outcomes and trends.
• Predictive analytic programmes are
already being applied to massive
datasets to spot trends and generate
insight around case behaviours.

15.
Premonition
• Predict the outcome of court cases
based on multiple criteria, including
the courthouse, the judge and type of
case.
• Help lawyers decide whether the case
is worth taking to court at all
• With a predictive analytic layer, a
system might not only find relevant
answers, but also chart the best course
of action.

16.
Lex Machina and Ravel Law
• Try to predict outcome probabilities using
data from prior cases
• Strategic insights include trends in case
timing, resolutions, findings, damages, and
remedies, as well as actionable intelligence
on opposing counsel, law firms, parties,
judges and venues.

17.
Machine Learning

18.
• Machine learning focuses on the
development of computer
programmes that can teach
themselves to grow and change
when exposed to new data

19.
• Machine learning algorithms are
designed to detect patterns in
existing data and then apply these
patterns to new data in order to
automate particular tasks.

20.
• An email spam filter is a basic
example as the machine learns
from user behaviour which
features of an email are likely to
constitute it as spam

21.
Machine Learning
and E-Discovery
• Computers can parse 1000s of digitised
documents in seconds.
• Spot relevant words and phrases, relationships
and patterns
• When reviewing documents machines can look at
every document – humans may look at a sample
• Machines don’t make mistakes and don’t get tired,
suffer eyestrain etc

22.
Advice as a
Commodity

23.
Using Legal
Expert Systems
• Allow automation of repetitive
aspects of legal work
• Not bespoke
• Can be standardised
• Repetitive
• Available 24/7

24.
Document
Automation
• Requires users to answer a series of questions on
a screen
• After completion of the online form a first draft is
made available
• Lawyers can pre-package experience
• Make it available to clients online

25.
Monetising Commodification
• Externalised service is chargeable
• Per use model encourages reuse
• Costing no longer based on hourly
rate
• Sitting behind the system is combined
not individual expertise

26.
AI and Litigation

27.
The
Online
Court

28.
Involves the innovative use of
technology to develop a new
process for litigation
Emphasis on conflict resolution
or dispute containment
Does not see a court hearing as
inevitable outcome
Uses an Internet based platform

29.
The Tiers
• Tier 1 – online evaluation
• Tier 2 – dispute resolution
interventions
• Tier 3 – The hearing

30.
Tier 1 and Legal
Expert Systems
• Web-based software interface would
guide the litigant through an analysis of
his or her grievance in such a way as to
produce a document or record capable
of being understood both by opponents
and by the court.

31.
Online Help
• Online help would be provided at every stage in
the process of completing the requisite online
documents
• Commoditised online advice as to the bare
essentials of the relevant law.
• “Commoditised advice” is a description of the
basic legal principles applicable to the litigant’s
dispute, rather than bespoke advice based up
the particular facts of the dispute and would be
provided by Legal Expert Systems software.

32.
The Online Courts Hackathon
• Gilbert + Tobin developed a system using
predictive analytics to help individuals assess the
merits of consumer law disputes.
• A team from Cambridge University developed a
machine learning system that predicts the
outcome of claims.

33.
Litigation Practice

34.
• Litigation work may be broken into
components
• Not necessary for the same lawyer
or team to handle each element
• Some aspects can be automated
• Some tasks may be delawyered
offshored or outsourced
• The unbundling of litigation
services

35.
Broken Down Transactional Elements
• Due diligence
• Legal Research
• Transaction Management
• Negotiation
• Bespoke Drafting
• Document Management
• Legal Advice
• Risk Assessment.

36.
Possible Futures
Re-Imagining Precedent in an AI World

37.
Two Scenarios
• Too much information – from principles to
facts
• Page Ranking and Precedential Value

38.
Too Much Information

39.
Precedent Technical
Pre-requisites
• A reliable recording system – print
• A common reference point
• A reliable law reporting system

40.
Technical
Problems
• Shelf space limitations
• What can be contained between
the covers
• A certain critical mass which if
exceeded makes precedent
unweildy

41.
The Digital Paradigm
• Enormous free to air databases
• Available via the Internet
• Where are the principles

42.
The Rear View
Mirror
• The law traditionally looks back to
precedent but the digital
environment means that the
depth of field is shorter, focussed
upon what is closer while infinity
becomes a blur.
• The problem is with the vast
amount of material that is
available, how can one maintain a
precedent-based system that will
rely upon dynamic changing
material rather than the reliability
provided by the printed law
report.

43.
AI and
Precedent
• AI analysis of caselaw data
• More likely to focus on factual similarities
• “Precedential” decisions will be those which align with
the facts of a case
• What happened to principle
• What is the ratio decidendi of a factually identical case.

44.
Precedent by
Page Ranking

45.
What is Page
Ranking
• PageRank is an algorithm
developed by Google and
used to rank websites in
Google search engine results.
It works by counting the
number and quality of links to
a page to determine a rough
estimate of how important
the website is. The underlying
assumption is that more
important websites are likely
to receive more links from
other websites.

46.
• Will frequent citation
determine the validity and
give added authority to a
case?

47.
• Within the world of predictive
analytics there is every possibility
that certain cases will appear more
frequently as authorities in a
particular field than others.

48.
• Is there a likelihood that predictive analytics
software will develop a form of ranking for
authorities depending upon the number of
times that they are cited.

49.
• The more a case is cited, the more
authoritative it becomes

50.
The combination of citation
frequency and predictive analytics
could well have an impact upon the
use of a case for precedential value.

51.
The Future
of Precedent
The answer to
the machine is in
the machine