Table Of ContentCONTRACTING AND CONTRACT LAW
IN THE AGE OF ARTIFICIAL INTELLIGENCE
Th is book provides original, diverse, and timely insights into the nature, scope,
and implications of Artifi cial Intelligence (AI), especially machine learning and
natural language processing, in relation to contracting practices and contract law.
Th e chapters feature unique, critical, and in-depth analysis of a range of topical
issues, including how the use of AI in contracting aff ects key principles of contract
law (from formation to remedies), the implications for autonomy, consent, and
information asymmetries in contracting, and how AI is shaping contracting
practices and the laws relating to specifi c types of contracts and sectors.
Th e contributors represent an interdisciplinary team of lawyers, computer
scientists, economists, political scientists, and linguists from academia, legal prac-
tice, policy, and the technology sector. Th e chapters not only engage with salient
theories from diff erent disciplines, but also examine current and potential real-
world applications and implications of AI in contracting and explore feasible legal,
policy, and technological responses to address the challenges presented by AI in
this fi eld.
Th e book covers major common and civil law jurisdictions, including the EU,
Italy, Germany, UK and the US. It should be read by anyone interested in the
complex and fast-evolving relationship between AI, contract law, and related areas
of law such as business, commercial, consumer, competition, and data protection
laws.
Contracting and Contract
Law in the Age of Artifi cial
Intelligence
Edited by
Martin Ebers
Cristina Poncib ò
and
Mimi Zou
HART PUBLISHING
Bloomsbury Publishing Plc
Kemp House , Chawley Park, Cumnor Hill, Oxford , OX2 9PH , UK
1385 Broadway, New York, NY 10018, USA
29 Earlsfort Terrace, Dublin 2, Ireland
HART PUBLISHING, the Hart/Stag logo, BLOOMSBURY and the Diana logo are
trademarks of Bloomsbury Publishing Plc
First published in Great Britain 2022
Copyright © The editors and contributors severally 2022
The editors and contributors have asserted their right under the Copyright, Designs and
Patents Act 1988 to be identifi ed as Authors of this work.
All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any
means, electronic or mechanical, including photocopying, recording, or any information storage
or retrieval system, without prior permission in writing from the publishers.
While every care has been taken to ensure the accuracy of this work, no responsibility for
loss or damage occasioned to any person acting or refraining from action as a result of any
statement in it can be accepted by the authors, editors or publishers.
All UK Government legislation and other public sector information used in the work is
Crown Copyright © . All House of Lords and House of Commons information used in
the work is Parliamentary Copyright © . This information is reused under the terms
of the Open Government Licence v3.0 ( http://www.nationalarchives.gov.uk/doc/
open-government-licence/version/3 ) except where otherwise stated.
All Eur-lex material used in the work is © European Union,
http://eur-lex.europa.eu/ , 1998–2022.
A catalogue record for this book is available from the British Library.
A catalogue record for this book is available from the Library of Congress.
Library of Congress Control Number: 2022933882
ISBN: HB: 978-1-50995-068-3
ePDF: 978-1-50995-070-6
ePub: 978-1-50995-069-0
Typeset by Compuscript Ltd, Shannon
Printed and bound in Great Britain by CPI Group (UK) Ltd, Croydon CR0 4YY
To fi nd out more about our authors and books visit w ww.hartpublishing.co.uk .
Here you will fi nd extracts, author information, details of forthcoming events
and the option to sign up for our newsletters.
PREFACE
Today we are witnessing the rapid development of artifi cial intelligence (AI), which
is enabling soft ware agents to carry out a growing variety of transactions inde-
pendent of human intervention. Some of these transactions include comparative
pricing, negotiating contractual terms, and buying and selling goods and services.
A broad question underlying numerous contributions to this book is whether the
use of AI in contracting can support or, perhaps in the near future, even replace
humans when it comes to entering into, draft ing, and executing contracts.
AI and other new technologies such as blockchain-based smart contracts
can remove various forms of human intervention from contractual transactions.
Accordingly, the law must fi nd a way to enforce transactions between AI and a
human contracting party where, strictly speaking, there is no ‘ consensus ’ or ‘ meet-
ing of the minds ’ in the traditional defi nition of these concepts. Th is is crucial
for machine-generated transactions where human intervention is absent from at
least one side of the transaction. Indeed, AI systems and tools in use today can
enter into transactions whereby the details of such transactions are unknown
to humans. In some cases, contracts are concluded with content that was never
explicitly intended, foreseen, or authorised by their programmers or users.
In these cases, such AI systems are not mere instruments for contractual transac-
tions. Th ey are not static vending machines that merely serve as conduits through
which contractual transactions take place.
While extra-contractual liability for AI has been widely analysed by scholars in
various jurisdictions, the specifi c impact of AI on our understanding of contract-
ing and contract law in this new age remains largely unexplored. Th is book thus
provides original, diverse, and timely insights into the nature, scope, and impact of
AI – particularly machine learning and natural language processing – in relation
to contracting practices and contract law.
Th e chapters of this book provide an in-depth analyses of a range of topical
issues, including how the use of AI in contracting aff ects key principles of contract
law (from formation to remedies); the impact of AI on autonomy, consent, and
information asymmetries in contracting; and how AI is shaping contracting prac-
tices and the laws relating to specifi c types of contracts and sectors. Th e chapters
not only examine these issues by drawing on key theories from diff erent disci-
plines, but also real-world applications of AI in contracting. Th e chapters further
explore possible legal, policy, and technological responses to the challenges that
AI poses in this fi eld.
Th e contributors to this book represent an interdisciplinary team of lawyers,
computer scientists, economists, political scientists, and linguists from academia,
vi Preface
legal practice, policy, and the technology sector. Furthermore, the book covers
major common law and civil law jurisdictions that are at the forefront of AI devel-
opment and application globally, including the European Union (EU), the United
Kingdom (UK), and the United States (US).
AI in Contractual Decision-making and Draft ing
Numerous chapters of the book explore inter alia how AI may help (and in some
cases, replace) humans in contractual decision-making and draft ing.
AI may be used in the pre-contractual phase to assess whether or not a party
should enter into a contract at all. Th is involves several aspects. AI systems can
encourage people to enter into a contract by identifying the potential needs to
be met by the contract. To this end, some companies and banks are using AI to
analyse prospective corporate mergers or takeovers. In the same vein, a company
may use AI for location-based intelligence to predict specifi c product purchases by
customers in a particular locality.
AI can help to enhance decision-making processes by analysing large datasets
that reveal a range of contractual risks. AI systems make it possible to undertake
due diligence of hundreds of thousands of documents in a very short time, and to
identify issues that need to be fl agged to a human reviewer. AI-driven risk analysis
tools can also be deployed to assess individual risk profi les. Several AI systems are
already in use by fi nancial and insurance institutions to evaluate their customers
in relation to whether or not a loan may be granted or an insurance policy taken
out, and on what terms.
In addition to risk analysis, AI can also improve the analysis of transaction
costs. Economists have examined how people decide between diff erent contrac-
tual alternatives on the basis of the related transaction costs. AI-driven pricing
soft ware can facilitate this decision-making process through intelligent data
analytical tools.
Moreover, AI can help with the analysis and draft ing of contract clauses.
AI-driven contract review soft ware, which analyses contract clauses, makes it
possible to identify certain risks. Applications of this type are already widespread
in several jurisdictions such as the US and UK. Nevertheless, many chapters of this
book point out that automated draft ing of contracts is still in its infancy. In fact,
most soft ware solutions currently available are based on standard templates that
have been adapted to the circumstances of an individual case with the help of ques-
tionnaires created by virtual robot assistants, so-called ‘ chatbots ’ . Another method
involves writing a contract directly in code to integrate with a computer system for
managing and executing contracts. Th e contract simultaneously becomes part of
the database that will later be used to train the AI system for draft ing contracts.
Finally, AI can support decision-making during the lifetime of a contract.
Although the use of AI is not yet widespread in this area, it is foreseeable that
Preface vii
relevant use cases will become more common. Such applications may be able to
evaluate the advantages and disadvantages of certain options available to parties
during the performance phase of a contract. AI may even help parties decide
whether or not a breach of contract is more advantageous than performing the
contract by predicting the respective costs of performance and non-performance.
AI in Contract Formation and Execution
Th e chapters of this book also examine use cases of AI in contract formation and
execution. We mention two scenarios here: fi rst, the role of AI as a contractual
agent, ie, acting on behalf of humans and/or other computers; second, the poten-
tial role of AI (in the future) as an autonomous contractual party.
First, AI can be considered an agent in contract law in the context of computer
systems that can be programmed by humans to make certain contractual deci-
sions. Th us, a machine can decide whether or not to conclude a contract if specifi c
predetermined conditions are met. Th is is the case with algorithmic trading, which
accounts for a signifi cant proportion of fi nancial transactions globally. Based on
hundreds of parameters, a computer system can decide the buying or selling of
a particular fi nancial product at a certain price, in a certain volume, at a certain
time, in a certain market, and under certain conditions. Moreover, AI systems may
also decide when and how to perform a contract. Th e mechanical execution of
contracts is well-known, for example, in beverage vending machines. In such a
case, no intelligence is required. Th e same applies to smart contracts involving
soft ware code that automates the execution of a contract.
Second, in the future, AI systems may become autonomous contractual parties.
Th is is where AI no longer acts as an agent for others, but for itself. It is autonomous,
in the literal sense of auto-nomos , which is governed by its own laws. To be party
to a contract, one must generally have the capacity to contract, ie, possess legal
personality. In a resolution adopted in 2017, the European Parliament supported
the idea of ‘ creating a specifi c legal status for robots in the long run, so that at
least the most sophisticated autonomous robots could be established as having the
status of electronic persons responsible for making good any damage they may
cause, and possibly applying electronic personality to cases where robots make
autonomous decisions or otherwise interact with third parties independently ’ .
At the dawn of a new era, AI has the potential to radically impact decision-
making in contracting, with signifi cant implications for contract law. Th is raises
serious research questions that numerous contributors of this book have analysed
from diff erent perspectives. For example, with what and/or whom is the human
contracting party transacting ? From a civil law perspective, a question arises as to
whether the contract is concluded by the interposition of a ‘ thing ’ or a ‘ person’ ?
Intuitively, we may see the interposition of a ‘ thing ’ in this case: the contract is
concluded by AI as an instrument in the same way that a phone or computer may
viii Preface
be used to enter into a contract. However, the use of AI here involves a certain level
of autonomy in contracting. So, should we conclude that, more than the interposi-
tion of a ‘ thing ’ , it is AI that actually realises the interposition of a form of ‘ person’ ?
In both civil and common law jurisdictions, the law already grants legal personal-
ity to n on-human contracting parties such as corporations.
Th e possibility to grant legal personality to AI systems is, however, disputed
among legal scholars. If we classify the AI system involved in the conclusion of
a contract as an ‘ agent ’ acting on behalf of the system ’ s user (the ‘ principal ’ ), this
could give rise to some practical problems. Notably, the expression of the user ’ s will
may be limited due to the autonomy of the AI. Depending on the circumstances,
the distribution of risks between the ‘ principal ’ and ‘ agent ’ may diff er. For instance,
if the AI system itself does not function properly or is poorly programmed and
makes bad decisions, the risks must be assumed by the developer. However, if the
bad decisions made by the AI system refl ect defects in a poorly designed contract,
risk sharing between the parties could be considered. In this latter case, it may be
necessary to provide certain legal protection to some contracting parties such as
consumers. For example, consumers may be given the right to an extended period
of time to cancel the contract, which would enable consumers to regain their deci-
sion-making power to some extent.
Th ird and important, Alan Turing, the father of modern computing, basi-
cally studied the following question: can machines think? In terms of contract law
theory, the relevant questions are: can AI systems make contracts, or more broadly,
make decisions instead of a contracting party ? Does such a machine have a place
in modern contract law ? Th e use of AI in contracting has the potential to shake up
core aspects of contract law theory, such as agreement, consensus, and the inten-
tion of the parties. For instance, even if we were to decide on policy grounds to
impose contractual liability on individuals who use or program AI systems, it is
diffi cult to regard these individuals as ‘ off erors ’ in the conventional understanding
of this concept. Such individuals have no idea if or when the system will make an
off er; they have no idea what the terms of the off er will be; and they have no easy
way to infl uence negotiations once the transaction is underway. Moreover, if we
consider relational contract theories, such as the work of Ian MacNeil, contracts
should be studied and understood as (human) relations rather than as discrete
transactions. AI systems have the potential of replacing the relational aspects of
contracts, such as trust, promise, consent, and enforcement.
Conclusion
Th e chapters in this book highlight the multifaceted and complex nature and
implications of AI in relation to contracting and contract law. Th ere is a dominant
discourse surrounding the use of AI to drive effi ciency in contracting, such as cost
reduction, process optimisation, and disintermediation. Th is economic logic can
Preface ix
directly clash with the law ’ s own logic and values such as the importance of good
faith, fairness, and protection of the weaker party to a contract.
We conclude with considering the risk of viewing AI as displacing the human
elements of contracts. Contracts are created by and for humans to undertake a
range of activities, even if contracts entail (non-human) legal persons such as
corporations. Furthermore, contract law pays attention to the human elements
of contracting, such as defects in consent and the intent of the parties. It remains
to be seen whether AI will marginalise these human elements of contracting and
contract law. As AI develops, it may remove humans from important processes of
decision-making in relation to contracting. Th is is already refl ected in algorithmic
trading whereby the assumption is that humans cannot make decisions in micro-
seconds (that is, in millionths of a second) when faced with vast amounts of data.
Th ere is a real danger in heralding the superiority of AI over humans in achieving
optimal economic outcomes in contracting. We believe that the human elements
are what essentially make contracts and contract law ‘ living institutions ’ that serve
a range of important market and non-market purposes.
Martin Ebers
Cristina Poncibò
Mimi Zou
