The impact of AI and Blockchain technologies in the Legal Industry

Innovation and Intellectual Property Law Student Name & Number: Hunter F. Thompson n8584222
Hunter F. Thompson
Law Student
Queensland University of Technology
Email: hunterfthompson@outlook.com
Phone: (04) 7907 7966
Dr. David Lindsay
General Editor
Australian Intellectual Property Journal
Journal Article Submission
Good afternoon Doctor Lindsay,
I submit the following article titled ‘Disruptive technologies and the law: The
implications of recent applications of Artificial Intelligence (AI) & Distributed Ledger
Technology (DLT) for legal practitioners’, to be considered for publication in the
Australian Intellectual Property Journal (AIPJ). This paper contains a 3000-word
analysis of the impact both DLT and AI are currently having on the legal industry.
The purpose of this analysis is to speculate, based on the already disruptive effect
these technologies have had, the rate at which these technologies will continue to
disrupt not only the legal sector but also other areas, such as finance and
agriculture. It is the further purpose of this paper to inform readers of the AIPJ,
particularly those in legal practice, of practical ways in which they can safeguard
their careers from disruption.
It is my aspiration to inspire readers of the AIPJ to begin to consider methods of
utilising disruptive technologies in ways which will greatly increase their value to their
clients and to re-assure legal practitioners that disruption is ultimately an incredible
opportunity for the legal industry to re-invent itself.
Kind regards,
Hunter F. Thompson

Disruptive technologies and the law: The implications of recent applications of Artificial
Intelligence (AI) & Distributed Ledger Technology (DLT) for legal practitioners
Hunter F. Thompsonª
ªLaw Student, Queensland University of Technology, Brisbane, Australia
Gartner Inc., a leading research and advisory company based in the United States that provides information
technology (IT) related insight for a range of industries, releases an annual ‘Hype Cycle’ which depicts the
symbiotic interaction between the hype surrounding emerging technologies and actual commercial activity
(e.g. investment, development and utilisation). Machine Learning (an AI technique) and Blockchain (a
component of DLT) have passed the peak of the hype curve within the last 12 months, meaning the ‘real’
activity will soon begin.1
1
Future Committee, The Future of Law and Innovation in the Profession (The Law Society of New South Wales,
2017) 33.

1.0 Introduction
Since the beginning of 2016, dissatisfied and distrusting of existing political
institutions, a majority of both US and British citizens utilised their voting power to
deliberately disrupt these institutions. This was achieved by the former through their
election of Donald Trump as their President and by the latter through their vote for
the United Kingdom to leave the European Union.2 The recent initiation of class
actions by Australian shareholders against both the Commonwealth Bank3 and
Shine Lawyers,4 demonstrates that financial and legal institutions are similarly being
scrutinised and held accountable.
Underlying the hostility being shown by participants in these systems of seemingly
omnipotent institutions, is an ever-growing frustration with the consistent breaches of
trust, monopoly on decision-making, lack of transparency, and avoidance of
accountability that have become the hallmarks of a system reliant on the
benevolence of centralised authorities.5 Increasingly exorbitant legal costs,
antiquated fee structures, unnecessary inefficiencies and restrictions on access to
justice due to drastic funding reductions to legal aid, are all factors indicating that a
re-invention of the provision of legal services is imminent. Two technologies which
threaten to facilitate radical change to the legal industry within the next decade are
AI software programs and the combined use of DLT and self-executing ‘smart
contracts’.
This article will now consider a more in-depth analysis of the disruption these
technologies are causing in the legal industry among others, as well as their
strengths and limitations, after which a conclusion will be drawn about the approach
lawyers must take to ‘disruption-proof’ their careers. It might be tempting for legal
2
Ibid 10.
3
Michael Janda, Commonwealth Bank faces 'very large' shareholder action on money laundering scandal (23
August 2017) ABC News http://www.abc.net.au/news/2017-08-23/commonwealth-bank-faces-shareholder-
class-action/8833860.
4
Katie Walsh, Shine Lawyers faces $250 million class action over market cap wipeout (27 September 2017)
Australian Financial Review http://www.afr.com/business/legal/shine-lawyers-faces-250-million-class-action-
over-market-cap-wipeout-20170927-gypmp4.
5
Future Committee, above n 1.

practitioners to start to believe these developments are the beginning of the ‘end of
lawyers’, but in the words of British legal futurologist Professor Richard Susskind,
dependent upon the adaptability of lawyers to new technologies, “the future for
lawyers could be prosperous or disastrous.”6
2.0 AI Lawyers – friend or foe?
An application utilising AI deployed this year by King & Wood Mallesons, is able to
assist international clients in determining whether a proposed deal requires Foreign
Investment Review Board (FIRB) approval.7 The program achieves this by allowing
lawyers to capture their expert knowledge within the program, and then replicating
the decision-making that lawyer would undertake to provide fact and context specific
answers to legal, compliance and policy questions.8 This application combines an
‘expert system’, 9 with other AI techniques including on-demand natural language
processing and machine learning.10
For lawyers, an understanding of expert systems and machine learning is important,
given these techniques have been developed and refined over the past two to three
decades, at a rate which justifies concern. A separate analysis of the different
applications of these techniques over time presents a practical example of the speed
with which technology adapts and changes, and why legal practitioners must not
hesitate to embrace the use of these technologies.
2.1 Expert systems
As an indication of their long-term value, American Express Company has
used an expert system to assist its credit authorisation staff sort through data
6
Richard Susskind, The End of Lawyers (Oxford University Press, 2008) 269.
7
Michael Mills and Julian Vebergang, ‘Artificial Intelligence in Law: An Overview’ (2017) 139 Precedent 35, 37.
8
Ibid.
9
Graham Greenleaf, ‘Technology and the Professions: Utopian and Dystopian Futures’ (2017) 40(1) University
of New South Wales Law Journal 302, 310.
10
Above n 7.

from up to 13 databases, since around 1988.11 Expert systems aim to
simulate human thought processes,12 using the experience ‘mined from the
jewels from expert professionals’ heads.13 These systems have achieved
some commercial success, being used to complete individual tax returns and
draft wills or simple contracts. These systems appear to succeed most in
areas with large markets, a lot of repetitive but necessary work, where there is
no need for frequent updating. For example, tax authorities around the world
have made good use of expert systems, which have significantly changed the
way in which tax professionals work.14
The Australian Government has utilised expert systems to assist in providing
individuals with information on visa categories for which they may be eligible
as well as providing advice on federal benefit entitlements.15 A more recent
example of an expert system is the chatbot DoNotPay, programmed by
parking ticket expert and 19-year-old MIT law student Joshua Browder.16
Since its launch in early 2016, DoNotPay has saved users $9.3 million
disputing 375,000 parking tickets, by asking a series of questions to
determine if the user can meet any exceptions for payment of the ticket.17
DoNotPay has expanded and now includes over 1000 chatbots that help
users fill out many more transactional forms, such as for maternity leave and
landlord contract violations. Browder has only recent stated his intent is now
11
Dorothy Leonard-Barton and John Sviokla, Putting Expert Systems to Work (March 1988) Harvard Business
Review https://hbr.org/1988/03/putting-expert-systems-to-work.
12
Alan Tyree, Expert Systems in Law (Prentice Hall, 1989) 1; Richard Susskind and Daniel Susskind, The Future
of the Professions: How Technology Will Transform the Work of Human Experts (Oxford University Press, 2015)
187.
13
Ibid 221.
14
Richard Susskind and Daniel Susskind, The Future of the Professions: How Technology Will Transform the
Work of Human Experts (Oxford University Press, 2015) 85-88.
15
Trevor J. M. Bench-Capon et al, ‘Logic Programming for Large Scale Applications in Law: A Formalisation of
Supplementary Benefit Legislation’ in Thorne McCarty et al (eds), Proceedings of the First International
Conference on Artificial Intelligence and Law (ACM Press, 1987) 190.
16
John Mannes, DoNotPay launches 1,000 new bots to help you with your legal problems (12 July 2017) Tech
Crunch https://techcrunch.com/2017/07/12/donotpay-launches-1000-new-bots-to-help-you-with-your-legal-
problems/.
17
Debbie Ginsberg, Expert Systems and Robot Lawyers (6 July 2016) IIT Chicago-Kent Law Library Blog
http://blogs.kentlaw.iit.edu/library/2016/07/expert-systems-robot-lawyers/.

to go after more complex legal processes like marriages, bankruptcies and
divorces.18
2.2 Machine learning
Machine learning as an AI method answers some of the limitations of expert
systems, and can tackle more sophisticated tasks previously assumed to
require human cognition.19 Generally speaking, machine learning involves the
application to a task, of computer algorithms that can ‘learn’ or improve in
accuracy and performance over time from a ‘training set’ of data, for the
purpose of making predictions.20 An example of a ‘training set’ might include
past matters run by a firm, in combination with published case decisions or
other private sources of data about case outcomes.21
The key difference between machine learning techniques and expert systems
is the emphasis on prediction.22 Expert systems seek to merely model the
decision-making processes undertaken by the expert whose experience the
system relies on. In contrast, machine learning algorithms utilise vast
quantities of data, pattern analysis and even its own past experiences
predicting to make decisions in a way that is unique to machines.23 Machine
learning algorithms are also not subject to the onerous requirement of
continuous updating required by expert systems, as they are able to adapt to
new available data, can search for new patterns and thereby improve
forecasting accuracy.24
18
Above n 16.
19
Harry Surden, ‘Machine Learning and Law’ (2014) 89 Washington Law Review 87, 88.
20
Peter Flach, Machine Learning: The Art and Science of Algorithms That Make Sense of Data (Cambridge
University Press, 2012) 3.
21
Surden, above n 19, 103-104.
22
Cary Coglianese and David Lehr, ‘Regulating by Robot: Administrative Decision Making in the Machine-
Learning Era’ 105(5) The Georgetown Law Journal 1147, 1156.
23
Ethem Alpaydin, Introduction to Machine Learning (MIT Press, 2014) 3.
24
Coglianese and Lehr, above n 22, 1159.

Today, these machine learning algorithms are used in a broad variety of
practical commercial applications, including fraud detection, data mining, self-
driving cars, product marketing, facial recognition and Internet search
results.25 Machine learning has notably been used in the financial sector to
predict the value of financial instruments and investments.26 Within the legal
industry, an intriguing but ethically questionable application of machine
learning is the field of litigation outcome prediction. A simple classification tree
machine learning algorithm was able to beat both experienced lawyers and
scholars in predicting decisions of the United States Supreme Court by a
margin of 16% (75% to 59%).27
2.3 Limitations of expert systems and machine learning
Expert systems are entirely reliant on the expertise with which they are
programmed, and the expert knowledge acquisition initially required to create
the large datasets on which these AI systems rely is incredibly time-
consuming and costly.28 A specifically legal expert system requires regular
updating, as new legislation or cases that alter legal advice must be
programmed into the system. Legal expert systems are also unfortunately
limited by foresight – if a particular situation or variable is not programmed
into the system, it will be unable to provide advice in circumstances involving
that situation or variable.
This limitation of foresight is of significant concern in circumstances where the
provision of advice requires the interpretation of contextual standards like
knowledge, reasonableness or intention, as it is logistically impossible to
25
Surden, above n 19, 89-90; Cary Coglianese and David Lehr, ‘Regulating by Robot: Administrative Decision
Making in the Machine-Learning Era’ 105(5) The Georgetown Law Journal 1147, 1147; Ian H. Witten, Eibe
Frank and Mark A. Hall, Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 3rd
ed, 2011) §
1.3.
26
Quintin Hardy, Wealth Managers Enlist Spy Tools to Map Portfolios (3 August 2014) New York Times
http://www.nytimes.com/2014/08/04/technology/wealth-managers-enlist-spy-tools-to-map-portfolios-html.
27
Theodore W. Ruger et al, ‘The Supreme Court Forecasting Project: Legal and Political Science Approaches to
Predicting Supreme Court Decision-Making’ (2004) 104 Columbia Law Review 1150.
28
Lyria Bennett Moses, ‘Artificial Intelligence in the Courts, Legal Academia and Legal Practice’ (2017) 91
Australian Law Journal 561, 563.

program every single situation or variable that might affect an assessment of
one of these standards.29
Similarly, while machine learning algorithms are touted and prized for their
accuracy, this boon comes at the cost of understanding the way in which
these algorithms interpret data, commonly referred to as ‘black-box’
procedures.30 The process by which an algorithm takes input data, analyses
patterns in the data and eventually associates certain characteristics of that
data with specific outputs, is well understood. However, the way in which the
algorithm reaches these conclusions or what exact characteristics the
algorithm is relying on are completely unknown to the user.31
This interpretative limitation means the use of machine learning may only be
appropriate where results that are ‘accurate enough’ are satisfactory, savings
in costs and efficiency are valued over an understanding of causality and
precision results,32 and where strong approximations are acceptable,33 which
would certainly not be the case for many applications of this technology in
legal practice.
Lawyers advising clients on large-scale, complex and nuanced matters are
unlikely to be replaced by a machine learning algorithm acting as a ‘proxy’ in
the decision-making process anytime soon, given the amount of contextual
considerations required to be made during the provision of advice in these
kinds of complex matters.34 Further, an understanding of the reasoning behind
the legal advice provided to them is crucial to corporate clients, from a legal-
risk prevention standpoint. Given corporations are relying more on their legal
29
Ibid.
30
Leo Breiman, ‘Statistical Modelling: The Two Cultures’ (2001) 16(3) Statistical Science 199, 199.
31
Leo Breiman, ‘Random Forests’ (2001) 45 Machine Learning 5, 5.
32
Coglianese and Lehr, above n 22, 1160.
33
Surden, above n 19, 97.
34
Ibid 97-98.

services for legal-risk prevention rather than dispute resolution,35 the
circumstances that trigger liability must be understood for appropriate policies
and procedures to be put in place to prevent these circumstances from
occurring.
Finally, a legal prediction application that uses machine learning algorithms
would only be useful to the extent the category of future cases it is being
asked to make predictions about, have relevant features in common with prior
cases it has analysed as part of its training set.36 Accuracy of prediction will
be markedly lower for future cases that present unique or novel facts
compared to those analysed in the past, meaning areas of law dealing with a
high amount of novel cases (e.g. criminal law) will likely be safe-guarded from
this technology for the foreseeable future.37
3.0 Distributed Ledger Technology (DLT) and Blockchain – redundant
intermediaries a reality?
DLT, which is often incorrectly referred to as ‘blockchain technology’, is currently
undergoing public testing by the Swedish government, with the hope that the current
Swedish land registry system can be replaced with a strictly digital system operating
on a distributed ledger.38 In Australia, Sydney start-up AgriDigital last year in
December successfully executed a live settlement of 23 tonnes of wheat, using an
automated ‘smart contract’ on a blockchain (a specific type of distributed ledger),
which enabled real-time payment on title transfer.
These two developments considered in unison suggest this technology may pose a
very real threat to the role of both lawyers and banks as necessary intermediaries in
35
Future Committee, above n 1, 17.
36
Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (Random House, 2nd
ed, 2010)
1-10.
37
Surden, above n 19, 105.
38
Valeska Bloch et al, Blockchain Reaction: Nine months on (Allens Linklaters, 2017) 10.

future real-estate transactions.39 An in-depth analysis of both distributed ledgers
generally, and the specific application of smart contracts, indicates that these
technological developments are still not ready for widespread application.
Inappropriate regulatory responses, practical limitations of coding, a lack of
appropriate dispute resolution and accountability procedures as well as uncertainty
surrounding governance frameworks and privacy concerns have all contributed to
delays in widespread adoption.40
3.1 Distributed ledgers
A distributed ledger is essentially an electronic database or ledger – an online
record of ownership of assets (e.g. shares, digital currencies, contractual
rights, physical assets or intellectual property).41 Perhaps the biggest benefit
of a distributed ledger over other electronic records is its decentralised nature
– copies of the distributed ledger are available to every single participant in
the network, meaning any recorded transactions are verified by receiving
consensus against all copies of the ledger.42 This process of verification
replaces the ‘trust’ element currently provided by banks or other central
authorities maintaining a sole authoritative copy of a ledger.43
The ‘blockchain’, is simply the history of transactions that have been entered
on a distributed ledger. While individual transactions are being verified they
are added to the blockchain as one transaction in a ‘block’ of transactions,
creating a permanent record of transactions that is open to every participant
to the blockchain.44 The potential benefits of the use of a distributed ledger as
a replacement for a physical title registry are clear. Just as a transaction is
39
Ibid.
40
David Rountree, ‘Navigating the Blockchain and the Law’ (2016) 26(9) Law Society Journal 72, 72-73.
41
Ibid 72.
42
Ibid.
43
Peter Yeoh, ‘Regulatory issues in blockchain technology’ (2017) 25(2) Journal of Financial Regulation and
Compliance 196, 196; Primavera De Filippi, ‘The interplay between decentralisation and privacy: the case of
blockchain technologies’ (2016) 9 Journal of Peer Production 18, 18-19; Primavera De Filippi and Benjamin
Loveluck, ‘The invisible politics of bitcoin: governance crisis of a decentralised infrastructure’ 5(3) Internet
Policy Review 1, 1-2.
44
Rountree, above n 41, 73.

entered onto a ledger, information related to the title to a piece of real property
could also be entered. Once entered and verified, the owner of that property
would no longer need to engage with the registry when transferring the
property, as each new transfer would simply add to the chain of title publicly
accessible on the blockchain.45
Participants in a such a system would no longer need to seek independent
verification from a physical registry, nor would they necessarily need to ‘trust’
each other, just the distributed ledger they are transacting on.46 In much the
same way that this application of DLT may facilitate the transfer of property on
a blockchain, so-called ‘smart contracts’ may allow a range of contractual
agreements to be carried out and recorded on a blockchain.47
3.2 ‘Smart contracts’
Dr. Gideon Greenspan, CEO of Coin Sciences Ltd. a leading blockchain
technology company perhaps best defines a smart contract as: “... a piece of
code which is stored on a blockchain, triggered by blockchain transactions,
and which reads and writes data in that blockchain’s database.”48 The above-
mentioned example of AgriDigital’s first live settlement using a blockchain
ledger demonstrates how a smart contract might operate. The terms, such as
the price, process of automatic weighing of the grain delivery, verification of
the funds through the blockchain and an automatic release of these funds to
the farmer on delivery, all had to be decided and agreed upon before being
turned into code and entered onto a blockchain.49
45
Alexander Savelyev, ‘Contract law 2.0: ‘Smart’ contracts as the beginning of the end of classic contract law’
(2017) 26(2) Information & Communications Technology Law 116, 119.
46
Ibid.
47
Brydon Wang, ‘Blockchain and the Law’ (2016) 19(1) Internet Law Bulletin 250; James Eyers, Blockchain
‘smart contracts’ to disrupt lawyers (30 May 2016) Australian Financial Review
http://www.afr.com/technology/blockchain-smart-contracts-to-disrupt-lawyers-20160529-gp6f5e.
48
Gideon Greenspan, Beware of the Impossible Smart Contract (12 April 2016) Blockchain News
http://www.the-blockchain.com/2016/04/12/beware-of-the-impossible-smart-contract.
49
Michael Bacina and Katrine Narkiewicz, ‘Smart contracts: just how clever are they?’ (2017) 36(8) Law Society
Journal 78, 79.

The potential benefits of these types of self-executing smart contracts are
two-fold. First, they make redundant the need for human involvement in part
or potentially all of the performance of an agreement, allowing a contract to be
concluded by the smart contract itself, as electronic agent for the parties.50
Second, they utilise decentralised blockchain technology to remove or
alleviate the need for a trusted third party or intermediary, and enable the
automatic execution of the contract without potential interference from a party
to the contract or a third party.51 In contrast to classic contracts where trust is
placed in the other party to the contract, in smart contracts, trust is instead put
into the computer algorithm which comprises the agreement (‘trustless
trust’).52
3.3 Limitations of DLT and smart contracts
Peer-to-peer, ‘trustless’ transactions, property transfers or smart contracts,
have significant limitations which may dissuade both clients and lawyers.
Initial attempts at regulating DLT, such as the New York State Department of
Financial Service’s ‘BitLicence’ have cost some blockchain utilising
businesses upwards of USD100,000,53 and been widely criticised as arduous,
complex and unnecessarily prescriptive.54 Similarly in Australia, as a result of
interpreted existing GST legislation to digital currencies, a 2014 ruling by the
Australian Tax Office (which is still valid), has created a situation in which both
parties to a digital currency transfers across a blockchain will incur GST.55
A further significant limitation of smart contracts appears when contracts rely
on human intervention to confirm the occurrence of a contractual obligation,
50
Ibid; Savelyev, above n 46, 121.
51
Bacina and Narkiewicz, above n 50, 79.
52
Savelyev, above n 46, 123.
53
Yessi Bello Perez, The Real Cost of Applying for a New York BitLicense (13 August 2015) Coindesk
https://www.coindesk.com/real-cost-applying-new-york-bitlicense/.
54
55
Ibid.

where the contract involves qualitative standards of performance not able to
be measured by lines of code.56 Once a smart contract is coded and entered
onto a blockchain it is virtually irreversible, meaning if appropriate care isn’t
taken, unlawful contractual provisions may be executed automatically, leaving
parties faced with two immediate practical difficulties.57
First, accountability must be attributed to either the lawyer who drafted the
terms, the developer who coded the terms into the smart contract or the other
party to the contract. Second, the party who has been wronged must pursue a
remedy, which will likely involve a lawsuit. (For a compelling case example of
a smart contract gone wrong, consider the hacking of faulty smart contract
code on the Ethereum Blockchain which led to the diversion of USD50 million
in 2016).58
Although a public distributed ledger may enable transparency and verification
between all participants, it also raises significant concerns in relation to
governance and privacy. Governance relationships will ultimately be decided
by the code underlying a specific ledger, therefore, if clients seek to utilise an
existing public ledger they will be subject to the rules attached to that ledger,
which are only able to be changed according to the will of the majority of
participants.59
The decentralised, public and anonymous nature of many pre-existing
distributed ledgers (e.g. the Bitcoin Blockchain) has driven many interested
business and individuals (and even some global banks) towards centralised
‘private’ ledgers. Private ledgers are limited to a pre-defined set of
56
Paul Gordon, Marni Hood & Henry Materne Smith, ‘Crypto-contracts: The coming of the blockchain
revolution’ (2017) 39(3) The Bulletin 34, 35.
57
Ibid.
58
Gaye Middleton, ‘The weakest link on the blockchain – smart contracts and The DAO attack’ (2016) 19(8)
Internet Law Bulletin 402; Rob Price, Digital Currency Ethereum is cratering because of a $50 million hack (18
June 2016) Business Insider Australia https://www.businessinsider.com.au/dao-hacked-ethereum-crashing-in-
value-tens-of-millions-allegedly-stolen-2016-6?utm_source=yahoo&utm_medium=referral&r=UK&IR=T.
59

participants, enable identification of participants and will likely placate
concerns participants may have about the risk of disclosure of a particularly
sensitive transaction, which is always a possibility on a public albeit encrypted
ledger.60
4.0 How can legal practitioners prepare for AI & DLT-related disruption?
The future of mass disruption in legal practice is certain: AI technologies will continue
to develop as algorithms become smarter, legal data increases in volume and
becomes more accessible due to cloud-based servers and computer power scales
infinitely as chips become faster and faster. Similarly, DLT is highly adaptable and
creative solutions have already been proposed that may answer some of its
perceived limitations in relation to dispute resolution and governance frameworks.61
Law, like many other human activities, is subject to the economic forces that bring
automation to all areas of life.62 Therefore, perhaps the most significant factor driving
change in the legal industry will not be technological developments themselves, but
an increasing demand from clients for faster, better and cheaper legal services.63
Faster is essential – clients are increasingly aided by technology, work and live in a
fast-paced environment and expect their lawyers to do so as well. Better is
increasingly important – we are living in a world climate where pre-existing
institutions such as the law are questioned and not entirely trusted. Lawyers are now
competing for client’s trust, against a widening pool of legal resources and services
available for free online (consider the DoNotPay chatbot example highlighted above),
and systems which facilitate ‘trustless’ and self-executing transactions that threaten
a lawyer’s role as trusted agent and dispute resolver. Finally, cheaper is crucial – not
60
Ibid; Gordon, Hood and Smith, above n 58.
61
Trevor I. Kiviat, ‘Beyond Bitcoin: Issues in Regulating Blockchain Transactions’ (2015) 65 Duke Law Journal
599-600; Adam Beck et al, Enabling Blockchain Innovations Through Pegged Sidechains (22 October 2014).
62
Thomas A. Smith, ‘From law to Automation’ (2016) 1 The Criterion Journal on Innovation 535, 542.
63
Michael Mills, ‘Using AI in law practice: it’s practical now; introducing the stunning and rapid advance of
artificial intelligence technology now being used in the practice of law’ (2016) 42(4) Law Practice 48, 52.

just for corporate clients who demand more for less, but for the ever-increasing
population of people for whom legal services have long been out of reach, because
of sheer unaffordability.64
To ensure their role in the future provision of legal services, only one option remains
– legal practitioners must endeavour to not only understand the technology that
threatens to meet the economic needs of their clients, and subsequently replace the
need for lawyers, but to augment their provision of legal services with it wherever
possible.
Word count: 2993 words (not including footnotes or first two covering pages)
N.B. Thank you for taking the time to mark my essay, and for co-ordinating a truly
thought-provoking and enjoyable subject with engaging assessment.
64
Ibid.

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