# The Ethics of Automating Legal Actors

Josef
Valvoda${}^{\leavevmode\raisebox{-0.70004pt}{\includegraphics[height=6.29996pt]{twitter_emoji/scale.png}}}$
Alec
Thompson${}^{\leavevmode\raisebox{-0.70004pt}{\includegraphics[height=6.29996pt]{twitter_emoji/scale.png}}}$
Ryan Cotterell
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Simone Teufel
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${}^{\leavevmode\raisebox{-0.70004pt}{\includegraphics[height=6.29996pt]{twitter_emoji/scale.png}}}$University
of Cambridge
${}^{\leavevmode\raisebox{-0.70004pt}{\includegraphics[height=6.29996pt]{twitter_emoji/scroll.png}}}$ETH
Zürich
{jv406, at808<EMAIL_ADDRESS><EMAIL_ADDRESS>

###### Abstract

The introduction of large public legal datasets has brought about a
renaissance in legal NLP. Many of these datasets are comprised of legal
judgements – the product of judges deciding cases. This fact, together with
the way machine learning works, means that several legal NLP models are models
of judges. While some have argued for the automation of judges, in this
position piece, we argue that automating the role of the judge raises
difficult ethical challenges, in particular for common law legal systems. Our
argument follows from the social role of the judge in actively shaping the
law, rather than merely applying it. Since current NLP models come nowhere
close to having the facilities necessary for this task, they should not be
used to automate judges. Furthermore, even in the case the models could
achieve human-level capabilities, there would still be remaining ethical
concerns inherent in the automation of the legal process.

##  1 Introduction

This paper discusses the ethical aspects of using natural language processing
(NLP) research to augment or replace the work of legal experts. It considers
common law legal systems and demonstrates that many proposals for automation
in the legal domain concentrate on the judiciary. Reflecting on this
sentiment, we find that a number of popular legal datasets are focused on the
text produced by judges. Whilst we agree that there are potential benefits to
the practical application of NLP to the legal domain, these applications face
several ethical challenges. Some of these challenges are resolvable with
technical advances, others, however, appear to be intrinsic to using any kind
of automation. We consider the two main legal actors, the judge and the lawyer
and find that while automation of either can be beneficial, lawyer automation
presents fewer challenges.

We begin by giving a brief legal background in § 2, where we explore the role
of judges and lawyers in common law legal systems and their distinct
functions. We then introduce the ideas of the rule of law and substantive
justice – the pillars on which the judicial system is built and which connect
law and morality.

In § 3, we explore practical proposals of legal NLP research. In our view,
these proposals can be broken into two groups; (1) Replacement with AI;
advocates a complete replacement of legal professionals with technology. (2)
Augmentation of judges or lawyers; suggest only partial automation or
supplementation of legal tasks with NLP. Concluding our background sections,
in § 4, we turn to legal NLP and discuss the impact of the shift from symbolic
to sub-symbolic AI on the field.

We then proceed in three stages. In the first stage, § 5, we begin by
outlining the risks and benefits of legal NLP. We suggest that current
proposals for implementing legal NLP face three technical challenges (1) lack
of contextual and social intuition at the trial stage; (2) inability to make
controversial moral and political decisions to develop the law; and, (3)
inability to justify the decisions to the public. Further, even if these
technical challenges are resolved, existing proposals pose unavoidable ethical
risks. They have the potential to (1) centralise power; (2) produce a more
brittle legal system and (3) undermine the accountability of policymakers.

In the second stage § 6, we turn to the main legal actors, the judge and the
lawyer, to evaluate their exposure to the risks and benefits. We suggest the
role of lawyers is a more promising domain for legal NLP by showing how the
various concerns affect lawyers differently. In a nutshell, lawyers are not
burdened with the task of being lawmakers. Instead, their role has more easily
measured metrics: whether or not they can convince judges and other lawyers of
the quality of their arguments. We recommend research paths towards resolving
the technical limitations of automating legal actors – be they lawyers or
judges.

In the third stage § 7, we turn to the role of the judge in contemporary legal
NLP research. Many existing and popular legal NLP datasets comprise
collections of cases, i.e. the text produced by judges. We demonstrate how a
lack of data about lawyers and their arguments can undermine the modelling
task built on top of these datasets. Therefore, we recommend legal NLP
researchers to focus more on the role of the lawyer in the legal system. Our
paper concludes with § 8 by contextualising our work in the wider NLP ethics
discussion.

Our proposals do not call for a wholesale reorganisation of the field. Judges
remain an important subject to study to further our understanding of law and
language. Indeed, the datasets built on publicly available case law have
opened up research in legal NLP to contemporary deep learning methods.111See
Caselaw Access Project: https://case.law Further, some datasets already
implicitly contain the voice of a lawyer, in the form of legal claims, waiting
to be disentangled from that of the judge. In fact, we hope that the shift to
a lawyer could, in some cases, be enabled by re-purposing existing datasets.
For example, Chalkidis et al. (2021) and Valvoda et al. (2023) predict both
lawyer’s claims and judicial outcomes, using the same legal dataset – an early
step in the right direction. We, thus, believe that focusing on the lawyer and
their interactions with the judge can supplement existing approaches, and
inspire new, more robust legal NLP research.

##  2 Legal Background

Our work primarily focuses on common law countries: this includes England, the
US, Australia, and India amongst others. The other major category of legal
systems is practised in civil law countries, such as France, Germany, Japan,
and Austria Zweigert and Kötz (1992). Both systems share a mixture of
important differences and commonalities. Overall, we focus on common law
countries for two reasons. First, these are countries with a rich history and
a longstanding philosophical debate surrounding them. Second, common law
systems, in particular England and the US, have been highly active in
practical proposals for NLP Cobbe (2020) and many of the datasets discussed in
§ 4 and § 7 involve common law material.

###  2.1 Legal Actors

Under both systems, there is a strict distinction between the role of judges
and lawyers. Members of the public go to lawyers to frame their needs in legal
terms; common examples are legal documents, such as wills, deeds, and
contracts. In addition, if the client wants to sue someone or is being sued,
the lawyer might be asked to assist. The client specifies their desired
outcome, such as avoiding responsibility for causing an injury. Lawyers then
translate these demands into legal allegations. An allegation is a legal
argument which suggests the client’s preferred dispute outcome is the most
consistent with the law Sako et al. (2022). These allegations are usually sent
to the opposing party’s lawyers before there is any litigation in the form of
a legal brief. The most common result is for the parties to then settle,
preventing the case from going to trial Sturge (2021). If the parties decide
to take it to court, the arguments will be repeated in front of a judge.

The central duty of the lawyer is to their client. They are responsible to the
public only to the extent they commit no crimes and fulfil their professional
obligations Armour and Sako (2020).222There are also lawyers who are employed
by the government. For example, public defendants’ salaries are paid by the
state. However, their obligation in court is to their clients rather than to
their employer. Finally, there are quasi-political legal roles, such as that
of Attorney Generals in the US, which blur the distinction between a lawyer
and a judge. Judges, on the other hand, are not hired by members of the
public. Instead, they work for the state and have a duty to the public to
decide cases correctly and fairly Wacks (2015). They are employed in large
numbers. Currently, there are $3,174$ judicial posts in England that are
spread out widely across the country Justice (2020), although this is small
compared to the number of lawyers.

The judge’s role emerges most clearly when parties litigate and go to trial.
Generally, trials have two stages. The first is fact-finding. Both sides
present their version of the facts to the court and the judge must decide
which version they believe is correct. A variety of different sources of
evidence are used, such as documents, fingerprints, and witnesses inter alia
Wacks (2015). In common law countries, the fact-finding role can also be
carried out by randomly selected members of the public called the jury
Zweigert and Kötz (1992).333The prevalence of the jury differs between common
law countries: in England, for example, they are present in many criminal
cases but few civil cases (cases involving contracts, torts, conveyances). In
the US, on the other hand, civil juries are more common in civil cases.

Second, after the facts are determined, the judge applies the law to determine
the outcome of the case. At this stage lawyers on both sides present their
arguments for what the law is and how it should fit the facts in a way which
will benefit their side. The judge then has to make a decision and explain it.
Crucially, the judge is constrained in their decision. They are only
considering the validity of the alleged violations presented to the judge, not
the applicability of any law. If the judgement is considered incorrect, it can
be appealed – it goes to a higher court for review Wacks (2015).444Typically,
when this occurs only the legal arguments are reconsidered and the facts are
assumed to be what was decided in the first trial.

In common law countries, the judge has a law-making role as well. Through the
doctrine of legal precedent, when a common law judge decides a case, they
create a new legal rule; future cases with similar facts to an already decided
case must be adjudicated by judges in the same way Black (2019). While past
cases are not binding in the civil law systems, they are still used to decide
new cases and can carry a certain weight under the principle of consistent law
application.

###  2.2 The Rule of Law

The Rule of Law is a fundamental political principle recognised by both common
and continental jurisdictions underpinning how the legal system operates. Many
different principles fall under the umbrella of Rule of Law. We will focus on
the following four.

##### Consistency.

It is a basic principle that like cases should be treated alike, and that
judicial biases should not interfere with legal decisions Fuller (1969). This
is not an absolute principle given the law sometimes needs to develop to meet
new societal needs, but it ensures the law is predictable and consistent
across cases. It is especially important for lower-tier judges who are
expected to dutifully apply the law set out by the courts above, such as the
Court of Appeal and Supreme Court.

##### Access to Justice.

Access to justice is another fundamental principle of the legal system Diver
(2020). Legal subjects must be able to gain access to legal advice and have
time in court to enforce their legal rights. Procedural delay, extremely high
costs, and geographically sparse courts are all hindrances to access to
justice and undermine the rule of law.

##### Equality Before the Law.

A central principle of the modern liberal state is that no one is above the
law, including lawmakers Dicey (1979). This means politicians and judges are
subject to the rules they make. Equality in this way improves the legitimacy
of law-making: legal subjects can be assured that there is one legal regime,
equally applied to them and the law-maker. The principle also acts as a check
and feedback mechanism for lawmakers: they feel the sting of unfair and unjust
legal rules because they are subject to them. Further, they can observe
firsthand how the rules operate in practice, gaining useful feedback for
creating new laws Dicey (1979).

##### Comprehensibility.

Finally, it is an essential principle that legal subjects can access and
understand the law which governs them Fuller (1969). This means the law must
be publicly available, understandable, and not contradictory. It also requires
satisfactory explanations behind why legal decisions were reached. This makes
it possible for legal subjects to follow the law, as well as giving them the
ability to challenge and criticise legal rules Hart (1961); Raz (1979).

###  2.3 Substantive Justice

The rule of law principles relate to the form rather than the content of the
law. The latter is a matter of substantive justice, which describes if the
content of legal rules is morally good or bad.

Law is connected to morality for three reasons Green and Adams (2019).555There
is a debate between legal positivists and natural lawyers on whether law must
be substantively just to qualify as law Gardner (2001). First, laws deal with
inherently moral issues, such as abortion, homicide, and constitutional
conflicts. Second, laws are often created to pursue moral ends, such as
reducing crime, furthering racial and gender equality, and redistributing
wealth in society. Third, law is a tool used by the state to coordinate
itself, and as such can be used for both good and evil at a larger scale than
would otherwise be possible.

Given these connections between law and morality, it is important to ensure
lawmakers are accountable to those they govern. A morally acceptable legal
system must have mechanisms of political accountability for lawmakers Dicey
(1979). For example, in democratic countries, legislators are held accountable
to the voting populace.666In the US judges are elected, much like politicians.

Overall, substantive justice is more difficult to assess than the rule of law
virtues. First, its content is inherently contestable. What constitutes a
morally acceptable law? Who should be responsible for holding lawmakers to
account? These issues are often socially and politically divisive and
constitute matters that reasonable minds can differ on. Second, what is
morally good changes over time. What was previously morally acceptable may
become unacceptable and vice-versa. A good lawmaker must take these changes
into account when making law to meet the evolving needs of society.

##  3 Legal Automation from a Policy viewpoint

A number of proposals exist on the future role of legal NLP Susskind (2008);
Alarie et al. (2016); Alarie (2016); Casey and Niblett (2016, 2021);
Goldsworthy (2019). We taxonomise these proposals into two groups: (1) Those
that advocate for the replacement of legal actors with technology, and (2)
those that advocate for mere augmentation. The former, more radical view
suggests NLP can be used to replace all legal tasks. The latter, on the other
hand, proposes legal NLP can only _supplement_ the work of traditional human
lawyers and judges, without completely replacing them.

###  3.1 Replacement

The most ambitious proponents of legal NLP suggest the entire legal profession
can be completely automated Cobbe (2020). There are broadly two approaches to
replacement. The first suggests a large amount of data, better models and more
computational power will allow the creation of highly effective legal NLP
models Alarie (2016). Under this view, the models will eventually be able to
give the correct answer to any legal question instantly with the benefit of
more information than any human lawyer or judge could ever possibly consider
Goldsworthy (2019). The decisions of this machine lawyer would not be directly
contestable by litigants or the accused, removing the need for courts and
lawyers.

Under the softer replacement approach, while all judges and lawyers are
replaced, a human remains in the loop as the policymaker Alarie (2016). A
small number of human policymakers would set general policy objectives, such
as to reduce traffic accidents by 40%. Legal NLP models with access to vast
quantities of data then design and implement rules to achieve these policy
goals Casey and Niblett (2016). As with the first approach, these rules would
be set automatically by the legal NLP model and there would be few, if any,
opportunities for appeal or adjudication. Without adjudication, the need for
lawyers is greatly diminished Casey and Niblett (2021). Instead of hiring a
lawyer and going to court, demands for change would have to be directed
towards governmental policymakers.

###  3.2 Augmentation

More conservative proponents view legal NLP as a tool to augment legal work
without entirely removing the need for human experts. Legal NLP tools of this
variety are already here and used widely by legal professionals, in particular
when carrying out due diligence, document review, regulatory compliance, and
e-discovery. In the UK, for example, 48% of law firms were using AI in their
business in 2018 Walters (2018). Tech startups cater to this extensive use of
legal NLP and argue its use can reduce the number of lawyers needed for a
particular job Frey and Osborne (2013). One proposal is to use legal question-
answering systems to reduce the cost of legal advice, and thus make it far
more available. Such technology could have important implications for access
to justice, allowing clients to obtain legal advice that they might otherwise
not be able to afford Pasquale (2019).

Another proposal is to employ AI to reduce uncertainty in estimating the
outcome of potential litigation. Firms have already begun to use software to
predict the likely outcomes of a lawsuit and could use this to advise a client
about their risks and liabilities Ellen Gregg (2019). On the public side,
several governments have started testing the augmentation of judicial
functions, such as sentencing and lower-tier tribunal decision-making in
criminal trials. The company Equivant, for example, offers an AI product which
predicts the re-offending rates of different criminals with weighted factors,
and is now used to make parole and detention decisions Hildebrandt (2020a).
Controversially, automated judges already sentence people in the US Kehl and
Kessler (2017) and China Stern et al. (2020).

Finally, some have proposed that augmentation could be implemented in the
hierarchy of the appellate court system Cohen et al. (2023). Under this
hierarchical view, the lower-tier judges, such as county court judges in
England, could be replaced with automated decision-making software. Higher-
tier judges, however, would be retained and would review decisions from the
automated decision-making tribunals under an appeal procedure. Further, if
reviewing judgments is cheaper and faster than producing them, it might be
possible to have a legal expert review each decision whilst still retaining
many of the benefits of automation. Such a hybrid system would have an
advantage of speed in the lower instances of the court, but ensure that legal
precedent continues to be developed by experienced human judges.

##  4 Legal NLP

Legal NLP can trace its origins all the way to the late 1950’s (Kort, 1957;
Nagel, 1963; Lawlor, 1963).777The field has gone by different names at
different times. Juris-informatics, legal informatics, legal artificial
intelligence or legal NLP are just a few. In this paper we stick with legal
NLP for consistency. One of the earliest symbolic AI systems, Hypo Ashley
(1988), explicitly encoded the principles of case-based reasoning in terms of
analogy and difference, often based on hand-extracted features. Many others
were inspired by it Aleven and Ashley (1997); Rissland and Skalak (1991);
Branting (1991).

Aleven (2003) originally introduced the task of predicting an outcome of a
case as an evaluation task for these models, which has nowadays become the
modern benchmark of legal NLP. For a long time, Issue Based Prediction (IBP;
Ashley and Brüninghaus, 2009), one of the successors of the Hypo model, held
the state of the art for this task. However, the reliance of the symbolic
systems on humans for both processing the input and encoding the changing
rules of law hindered their wider adoption. This became a problem for the
deployment of these models since the law can change constantly with every new
court decision.

Recently the popularity of machine learning, combined with a desire for more
robust models of law, has rejuvenated interest in developing applications for
the legal domain. This shift from symbolic to sub-symbolic legal AI has
implications for contemporary legal NLP research. Instead of focusing on
encoding legal reasoning by hand, the aim is to approximate it from legal
data. Since the architectures powering many SOTA approaches in legal NLP are
fairly homogeneous, the choice of data the ML models are trained on becomes
very important.

The ML tasks trained on legal text include question answering (Monroy et al.,
2009), legal entity recognition (Cardellino et al., 2017), text summarisation
(Hachey and Grover, 2006), outcome prediction (Xu et al., 2020; Zhong et al.,
2018; Aletras et al., 2016) and majority opinion prediction Valvoda et al.
(2018), legal topic classification Nallapati and Manning (2008), court opinion
generation Ye et al. (2018), case citation resolution Shaffer and Mayhew
(2019), study of legal precedent Valvoda et al. (2021) or legal consulting
Wang et al. (2019). For a comprehensive overview of legal NLP, see Zhong et
al. (2020a).

Yet another strain of work has emerged with an explicit focus on modelling
individual judges by sociodemographic and similar features, instead of legal
text. Katz et al. (2017), who predicts the outcome of the United State Supreme
Court case law, primarily use the meta-data of the court. Employing the
nearest neighbour algorithm, they achieve 70% accuracy using features such as
date, court position in the court hierarchy, judge names and lower court
outcome.

##  5 Ethical Dimensions of Legal NLP

We believe there are both benefits and risks in the use of NLP in the legal
domain. The benefits are large enough that refusing to use NLP could present a
moral failure akin to not using new medical treatments to treat patients. On
the other hand, the ethical risks of NLP are also significant and need to be
weighed up against these potential benefits.

###  5.1 Benefits of Legal NLP

We see three major benefits that legal NLP can bring to the rule of law and
the substantive content of the law: (1) Accessibility; (2) Consistency; and
(3) Capacity. We consider each in turn below.

##### Accessibility.

Technology could improve access to justice in the following three ways. First,
automated legal services could be created and delivered more quickly than
human advice – enabling fast resolution of legal disputes. Second, the price
and cost-unpredictability of legal services could significantly decrease if
aspects of legal reasoning are automated. Third, legal NLP could facilitate
geographically wider availability of legal services by no longer being bound
by the proximity to the physical location of the providers of legal services.
The last point has been especially useful during the pandemic, when many
courts adopted a hybrid or fully online operation regarding some of their
hearings.

##### Consistency.

There is a high variance in the quality of human-provided legal services. In
practice, this means that different people have access to different quality of
legal advice, and related cases might not be decided in similar ways. Legal
NLP could help play a role in narrowing the gap between different legal
actors. Ideally, this could bring us closer to the ideal of an equal and
consistent legal system.

##### Capacity.

NLP could help lawyers, judges, and litigants deal with growing legal
complexity. Over time, more legal sources are produced, at a greater rate, in
more detail, and with greater varieties of legitimacy Schuck (1992). Human
lawyers have found this growing mass of material difficult to handle
Hildebrandt (2018). The growing volume of legal datasets suggests the quantity
and complexity of legal work will only increase. However, legal NLP could also
help to reduce this complexity and allow for deeper and wider research than
any human alone would be capable of Alarie (2016). For instance, the capacity
of large language models, such as GPT-4 OpenAI (2023), to synthesize a scale
of data beyond human comprehension might help to address this problem in the
future.

###  5.2 Risks of Legal NLP

In addition to benefits, there are also important ethical risks in using NLP
in the legal domain. These can be grouped into two basic categories: technical
challenges and inherent challenges. The former relate to technical limitations
in current legal NLP technology in achieving what skilled lawyers and judges
can do Clayton and Boyd (2020). These are, in theory, resolvable with better
models and improvements in the capacities of NLP. The latter are inherent
challenges which arise with the use of technology in the legal process. They
capture the ethical and political challenges that remain even if NLP can
perfectly replicate legal tasks.

####  5.2.1 Technical Challenges

We discuss three technical challenges of legal NLP: (1) the need for human
intuition at trial; (2) the presence of moral and political debate in the
legal process; and (3) reason-giving in law.

##### The Trial Problem.

author=josef,color=blue!40,size=,fancyline,caption=,]added connection to
Bender’s octopus As noted in § 2, a central part of a trial is the fact-
finding stage. For a machine to find evidence, all that is required at this
stage is combing through large quantities of textual data Quezada-Tavárez et
al. (2021). Current NLP techniques excel at this task. Some types of evidence,
however, pose serious problems. In many cases, the legally relevant factors
are mental states, for instance intent, foresight, and malice, which are
inherently subjective. The judges also have to assess psychological
characteristics of defendants or plaintiffs, such as their overall credibility
and honesty, and whether they seem violent, reckless, regretful or violent.
Trying to determine automatically if these mental states are present, is a
highly nuanced process, which cannot currently be done well from text alone
Tortora et al. (2020); Petoft and Abbasi (2020).

To be able to use NLP models in court the models will need to uncover the true
meaning of the situation at hand. The lack of grounded understanding is a
well-known issue in the wider NLP discourse. For example, Bender and Koller
(2020) discuss the limitations of NLP models in accessing meaning from text
alone in their _octopus test_.

##### The Moral Dimension of Law.

author=josef,color=blue!40,size=,fancyline,caption=,]integrated Leins in here
Creating substantively just law requires a deep understanding of morality,
politics, and changing social conditions. A system that has been trained on
past legal material, as all current ML-based NLP models are, is likely to be
backwards-looking and will not be able to take an active part in evolving the
law to meet the needs of society Markou and Deakin (2020). Further, in common
law systems, the legal system has a fluid aspect, and an important judicial
role is re-interpreting existing precedents in light of current social needs
Delacroix (2022). For example, the highly controversial case of Roe v Wade
required the US Supreme Court to combine a mixture of modern moral and
political considerations with the technical legal interpretation of the
Constitution. The case was made complex not only by the difficult moral debate
over abortion, and the political question of appropriate federal state power,
but also legal questions over the correct way to interpret the Constitution in
a changing society. As Talat et al. (2022) and Fraser et al. (2022) point out,
there are problems with modelling morality with NLP methods. Current NLP
models of morality only learn about moral situations from a limited context,
restricting their capacity to make complex and important moral decisions.

Related to this issue is the work on ethics and legal NLP by Leins et al.
(2020). In their paper, the authors question the ethics of collecting legal
data in the first place. Pertinent for our discussion above, are their
concerns about updating the datasets when legal decisions get reversed or a
case is appealed to a higher court. The danger is that a ML model will always
lack behind the latest developments of the legal doctrine. Furthermore, in the
wider NLP discourse, there is an ongoing discussion about how biases in the
datasets used to train NLP models can get exaggerated by the model Bender et
al. (2021). A legal NLP tool acting upon such biases would have severe
repercussions in the legal domain. Especially if such a tool is to aid in
upholding moral values.

##### The Justificatory Role of Law.

author=josef,color=blue!40,size=,fancyline,caption=,]contestability, wider
discourse Legal decisions are the product of the debate between two parties in
a matter that is often highly contentious. The resolution of this debate is
important and must be comprehensible and justifiable to the public. Current
legal NLP models struggle to give reasons for their decisions. This raises two
problems. First, without providing a clear explanation, NLP models will be
unable to convince the parties to the dispute that a correct decision has been
reached. Second, a central part of the rule of law is the contestability of
any legal decision Delacroix (2022). It is important that everyday citizens
are able to participate in, understand, and challenge the decisions which
govern them. A legal system that cannot explain the outcomes of cases could
severely restrict this possibility Hildebrandt (2020b).

The issue of contestability of a machine learning system goes beyond legal
applications and it has been discussed in the wider NLP literature Mitra
(2021), as well as in the context of human-computer interaction Hirsch et al.
(2017) and social computing Vaccaro et al. (2019). There are also general
limitations and privacy concerns regarding to the use of LLMs which we discuss
separately in § 8.

NLP has made huge advances in the past decades, and it hopefully will continue
to do so. As NLP advances, we expect the technical challenges to get gradually
resolved or at least ameliorated.

####  5.2.2 Inherent Challenges

We further foresee three inherent challenges of using technology in the legal
domain: (1) the centralisation of political power, (2) increased brittleness
and (3) lack of accountability. Unlike technical challenges, no technical
advance in NLP will resolve the inherent challenges.

##### Centralised Power.

The benefits of using NLP in the legal system – whether it is accessibility,
consistency or capacity – lie in the scalability of the technology. The
underlying assumption is that with technology, there will be fewer human legal
actors involved in any single legal process. The flip side of these benefits,
however, is the risk of centralising power. Without AI, the expertise required
to adjudicate legal cases is possessed by judges and lawyers Cobbe (2020), and
it is held in a distributed manner. These actors work separately from one
another and most of them are not coordinated in one group or location at any
time. They can therefore act as checks on one another. Replacing and
augmenting legal expertise with technology will increase efficiency, but
likely result in a situation where there are fewer individual actors in the
system. This creates the risk of upsetting the current fine-tuned balance:
fewer legal agents could operate with fewer constraints and checks between one
another.

##### Increased Brittleness.

The risk of centralising the legal system also raises a danger of increased
fragility. There are two aspects to this. First, there are the risks
associated with making the legal system more dependent on digital
infrastructure. The legal system could become more prone to technical failures
such as electrical outages, and cyber-attacks. Taleb (2012) has described this
kind of social system as fragile: it cannot accommodate unusual or extreme
events.888There is naturally a trade off here. Increased accessibility through
technology makes the legal system more robust to failures of physical
communications for instance. The risk of widespread system failure increases
proportionally to the level of legal NLP involvement in the legal system.

Second, a major benefit of successful automation is the speed-up of the entire
legal process. A fast legal system will also result in the fast creation and
application of new precedents. This reduces the margin of error in law-making,
as there is less time to review, challenge, and test new law before it is re-
applied. In areas where predictability is crucial, such as land law and
contract law, small errors could be devastating if applied across the legal
system. Therefore, it is essential that decisions must be reached with a high
level of reliability. Legal NLP, therefore, raises a political risk that
unjust laws will be applied more quickly and comprehensively, with fewer
checks at the application stage.

##### Lack of Accountability.

Legal actors must always be held accountable for their decisions, following
from the legal principle of equality before the law (§ 2.2). Currently, legal
decisions are made by identifiable human experts. This ensures that specific
individuals can easily be identified and challenged for their decisions, both
by members of the public and by private clients Diver (2020). A risk of
introducing legal NLP in the system is that it makes it more difficult to
identify who is responsible for specific decisions.

The problem of accountability has been raised with respect to automation at
large and in particular in the context of self-driving cars and recidivism
prediction tools Gless et al. (2016); Tonry (2014); Ryan (2020). We can take a
lesson from these contexts to identify what might be the risks facing legal
NLP in the future.

The context of self-driving cars illustrates the complexity of attribution
when it comes to partial automation. The liability for self-driving cars
typically depends on the level of automation and user control Ryan (2020);
Boeglin (2015). There are six levels of automation.999The SAE Taxonomy and
Definitions for Terms Related to On-Road Motor Vehicle Automated Driving
Systems breaks down into six levels (0-5) and can be found here. For our
purposes, Levels 1-4 are particularly interesting. In brief, Level 1 (Driver
Assistance) and Level 2 (Partial Automation) describe a vehicle with the
ability to support the driver in steering, braking and accelerating. Level 4
(High Automation) is where the car is capable of fully driving itself in some
driving modes (for example on a highway). Level 3 (Conditional Automation) is
in between. The car is somewhat self-driving, but a human must be ready to
intervene at any point.

Manufacturers will usually deny responsibility at the lower levels (Level 1
and Level 2). Conversely, they will take responsibility for crashes occurring
at Level 4. There is considerable uncertainty at Level 3, with the driver
sometimes being found liable for reduced damages, whilst in other cases
escapes liability altogether Ryan (2020). While developed in the context of
self-driving cars, the above taxonomy might be a useful method of assessing
where the pitfalls of deploying legal NLP might lie. Level 3 system legal NLP
system would be one where automation of an aspect of legal reasoning takes
place, but humans are expected to monitor the system for potential failures.

The latter category, recidivism prediction tools, can shed some light on how
difficult it is to challenge legal decisions aided by software. In the US,
algorithmic risk assessment tools have been employed to aid judges in making
predictions about the risk of the defendant re-offending before the
trial.101010There are many tools currently used. For example, Correctional
Offender Management Profiling for Alternative Sanctions (COMPAS); the older
Historical Clinical Risk Management-20 (HCR-20); Violent Risk Assessment
Guide-Revised (VRAG-R); and Sexual Violence Risk-20 (SVR-20) Since the
deployment of these systems, the validity of their use in the court has been
challenged several times by applicants alleging an infringement of their
constitutional rights.

For example, in the case State v. Loomis, the accused claimed that the use of
risk assessment tools to calculate his criminal sentence breached his rights.
One of his arguments was that the software, due to its proprietary nature, was
unaccountable and precluded him from challenging its scientific validity
Thomas and Ponton-Nunez (2022). The court rejected this argument on the
grounds that, whilst the algorithm was hidden, the data it used was all
publicly available.

Worryingly, the courts presiding over these cases have not found the lack of
transparency as an infringement to the right of the parties involved Thomas
and Ponton-Nunez (2022). Instead, they are mostly sanguine about hidden
algorithms and the use of personal characteristics, such as gender, to
calculate sentences. Their reasoning relies upon the fact that, much like in
the case of self-driving cars, where automation is only at Level 1 or 2, there
is an identifiable human (the trial judge) who is primarily responsible for
the relevant decisions.

If these early cases are anything to go by, challenging a machine-made
decision on the basis of accountability will be difficult, especially where
such decisions are made using proprietary technology. Further, such challenges
are apt to get more complicated if the extent of automation is greater, and
resolving who should take responsibility in such a situation will require
inherently political choices Ryan (2020). Should it be the responsibility of
the researchers who have designed the legal NLP system, the software engineers
who have implemented it for the provider, or the provider of the service
Morison (2020)?

Furthermore, as a matter of equality before the law, it is important for those
responsible for the decisions to feel the effect of the laws they create.
System administrators are far less exposed than trial judges to the specific
decisions and outcomes their tools produce. The questions of accountability in
legal NLP, therefore, go beyond technical concerns.

##  6 Judge vs. Lawyer

Since lawyers and judges both practice law, using technology to automate
aspects of their roles should give the public wider access to more consistent
and better-informed legal services, and should overall benefit the
accessibility, consistency and capacity of the legal system. Both the
automation of the lawyer and the judge would result in these positive effects.
We will now discuss the roles with respect to the risks, and whether they are
also equally balanced between the two primary legal actors.

###  6.1 Technical Challenges: Judge vs Lawyer

Let’s first consider the technical challenges of automating judges and
lawyers.

##### The Trial Problem.

The judge and the lawyer have different roles during the trial. The judge is
responsible for assessing witness testimonies, a process where they examine
inconsistencies, reactions, and emotions from the oral accounts of witnesses
and litigants. The judge, therefore, must be able to read diverse physical
cues and contextual social knowledge, skills that are difficult to replicate
through textual learning. These are qualities that current legal NLP tools
lack. The role of the lawyer, on the other hand, is to ascertain their
client’s side of the story, assess their opposing counsel’s evidence,
communicate with clients and processing ambiguous facts. They also need to
assess the changing reactions of a jury to arguments. All of this requires
emotional and social sensitivity. Finally, lawyers require the ability to
decide how to cross-examine witnesses, and create compelling narratives,
tailored for the particular judge or jury composition Brooks and Gewirtz
(1996). Therefore, although their roles are different, lawyers and judges face
similar difficulties when it comes to assessing standard legal evidence.

######  Recommendation 1 (Multi-modality)

Legal NLP needs multi-modal approaches, integrating vision and sound, which
are necessary for the full automation of judges and lawyers.

##### The Moral Dimension of Law.

The requirement of substantive justice in law means lawyers and judges
inevitably need to consider ethical and political factors. Many cases involve
morally charged issues and judges are under a duty to the public to reach
morally correct decisions. Since judges take moral factors into account,
lawyers must also include them in their arguments and indicate the ethical
context of their client’s situation Liu (2022). We believe that the role of a
judge as a moral arbiter sets a high burden for the full automation of judges.
The metric for morally acceptable law is highly contestable and controversial.
A successful judgment under this criteria must be persuasive to a wide range
of groups, ranging from litigants, politicians, academic commentators, and the
public generally. Given the controversial nature of moral judgments, these
groups are likely to diverge, and deciding the correct weight to be accorded
to each is also contestable. This makes it extremely difficult to determine
whether a model is successful in making morally good decisions.

In contrast, a lawyer only needs to successfully appeal to the decision-making
of the judge rather than create an argument they themselves believe to be
morally sound. It is not their primary responsibility to ensure law is
enforced according to moral standards accepted broadly by different groups in
society. Rather, their role is to present their client’s case in the
strongest, most persuasive form possible to convince the judge to decide in
their favour.author=josef,color=blue!40,size=,fancyline,caption=,]changes
regarding lawyer and morality A good argument, under this standard, is one
which generally persuades judges and secures victories in lawsuits for the
client. Lawyer automation, therefore, has a lower technical burden to overcome
when it comes to modelling morality.

######  Recommendation 2 (Morality)

We suggest following the recommendation of Talat et al. (2022) of including
domain experts in developing the moral sense of NLP models.

##### The Justificatory Role of Law.

Both lawyers and judges must explain their decisions. However, their
justifications serve very different ends. Judges explain their decisions
because of the rule of law principle of comprehensibility. It is important for
the members of the public to understand why a judgement was reached, both for
its legitimacy and so they can contest it. Justifying decisions to the public
to this level sets a very high technical burden to overcome. It requires
feedback from a wide range of public actors and interest groups. Reasonable
minds can differ on whether an explanation is comprehensible. Furthermore, it
takes a long time to assess the quality of the judge’s reasoning and whether
it has shaped the law in a positive or negative way. Lawyers on the other hand
give justifications for more practical purposes. The main test is whether
their arguments can persuade judges and other lawyers. This means that there
is a more easily measured criterion of success: If other lawyers and judges
accept and understand the arguments, the justifications have succeeded. If
they do not, they need improvement. The burden of automating a judge is
therefore higher than that of automating a lawyer.

######  Recommendation 3 (Explainability)

Legal NLP models need to explain their reasoning in a way that allows members
of the public to exercise moral and political scrutiny over it.

There are outstanding technical challenges for the full automation of the
judge or lawyer. In light of these challenges, we believe that augmentation is
a more realistic pursuit given the current limitations of legal NLP. At the
moment, we should leave aside the witness assessment, moral decision making
and justifications to human experts. Nonetheless, we see the pursuit of the
full or partial automation of lawyers as less problematic than that of judges.

###  6.2 Inherent Challenges: Judge vs Lawyer

Now we turn to the challenges that cannot be resolved by technology alone.

##### Centralised Power.

author=josef,color=blue!40,size=,fancyline,caption=,]natural monopoly vs. mane
made one The risk of centralising power is much greater if judges are
automated than if lawyers are automated. This is for two reasons: the
political power judges possess, and their organisational structure. First, as
noted in § 2, judges in common law countries possess law-making power, which
gives them influence over the lives of everyday people. The role of a lawyer
is different. Lawyers do not create new laws, they only make arguments that
the judges take into account. This considerably limits their ability to change
the law. Second, judges are more likely to be centralised than lawyers. As
state employees, they are by default employed by a single organisation: the
government. Additionally, the role of the judge involves high consistency
which has tempted some to believe that a single automated decision-making
system is desirable in the first place, see § 3.

Conversely, lawyers are organised in private firms which compete with one
another for the business of clients. Law firms are free to structure
themselves and tailor the advice they give to their clients. As a result, law
firms are more likely to operate independently, choosing their own tools and
approaches, whether they are aided by technology or not. Consequently, it is
unlikely that the automation or augmentation of the lawyer profession would be
achieved using a single NLP model or tool, and therefore the risk of
centralising power is lesser when it comes to their role in the system.

Nonetheless, they are still at risk of centralisation. Legal NLP, like other
technology sectors, might demonstrate network effects and lock-in, leading to
one or two providers dominating the market. This risk, connected with the
potential effect on the development of the law and the accessibility of legal
advice, means centralisation remains a concern when automating either actor.

##### Increased Brittleness.

Increased centralisation also brings the risk of increased brittleness. Since
judges are more at risk of the former, they are also more susceptible to the
latter. The importance of role of the judge as a lawmaker magnifies the
potential harm that could arise from a failure or malfunction of the tools
they use and the aspects of their role which are automated. While a human
judge can be biased, incompetent, or subversive, they can be checked by other
judges. Deferring aspects of judicial reasoning to a machine threatens self-
policing among judges. Furthermore, the speed and scale at which automated
judicial reasoning could cause harm is much greater than in the case of a
biased human.

Partial replacement helps mediate some of these issues by introducing human
review. The extent this mediates the risk will depend on the extent humans are
placed in the loop, producing a trade-off between speed, cost, and fragility.
In contrast, lawyers have less influence on the system as a whole, and there
is a lower risk of their centralisation. Therefore, there is a lower risk of
brittleness in the case of their replacement or augmentation. Clients who are
able to choose from a variety of legal service providers can avoid bad actors;
faults in tools used by one law firm is therefore unlikely to spill over into
the others.

######  Recommendation 4 (Diversity)

It is necessary to maintain the diversity of adjudicators in order to avoid
the risks of power concentration and brittleness. Whether machine or human,
having the same automated system for every case poses a danger of systemic
malfunction and fragility.

##### Lack of Accountability.

The judge and lawyer are both accountable, but they are accountable to
different groups. The judge is accountable to the public and state officials,
whereas lawyers are accountable directly to their clients. This distinction
has implications for the automation of lawyers and judges. Law firms must meet
the needs of their clients; they are liable for malpractice and must deliver
services which are competitive in the legal services market. As a result, they
can naturally act as the site of responsibility should their use of legal NLP
fail. In contrast, judges are accountable to the public and state officials.
There is no law firm to shift the responsibility to, nor a market of legal
customers who can evaluate the success of legal NLP in meeting their needs.

In the most severe case of total replacement, it might become difficult to
identify individuals responsible for the decision made by a machine judge.
There are also issues of equality before the law. A machine judge which is
used to automatically decide cases and, therefore, make new precedents, will
have no experience of the law it enacts. This raises issues of the legitimacy
of their decisions. A machine will lack the basic feedback process present
when a judge experiences the effects of the cases they decide on. Since
lawyers do not create law, their legal NLP replacement does not pose this
risk.

Even if the use of AI in the court is limited, accountability remains an
issue. If current recidivism prediction software is anything to go by (see §
5.2.2), it will be difficult to challenge any negative impact a statistically
driven tool might have on the judge’s decision. Currently, a claimant has no
right to examine the tool that is used to help to decide their legal faith and
can only hope that the judge’s understanding of the technology is
sophisticated enough to account for the biases and errors such a tool might
introduce.

######  Recommendation 5 (Accountability)

It is necessary to decide who is accountable for the actions made using legal
NLP.

Resolving the inherent challenges will require careful consideration of how
exactly is the technology to be introduced in the courtroom. While the hybrid
augmentation approach seems the most sensible, a danger remains that the human
in the loop will overestimate the abilities of the NLP tools and defer to
them. We find this particularly concerning when it comes to the automation of
the role of a judge.

##  7 The Voice of a Judge in NLP

In the above discussion, we argued that the automation of the lawyer poses
fewer challenges than the automation of the judge. Nonetheless, the tasks
lawyers and judges conduct during their work are closely connected. In this
section, we explore several legal NLP tasks and demonstrate how the role of a
judge is inadvertently modelled via reliance on judge-created training data.
We then make suggestions on how these tasks could be redefined to prioritise
the voice of a lawyer.

###  7.1 Legal NLP Tasks

The three tasks we consider in this section are: Legal Outcome Prediction,
Similar Case matching, and Legal Question Answering. Each task relies on
judge-generated text for training data. Since the major paradigm in legal NLP
is ML, the current operationalisations of these tasks turn them into a series
of different approaches for modelling a judge. It is worth noting from the
outset that these tasks and their accompanying datasets are not an exclusive
list of legal NLP research directions; see § 4. Nonetheless, these are tasks
used in well-established legal benchmarks, such as LexGLUE and COLIEE
Chalkidis et al. (2022); Rabelo et al. (2020), and thus deserve our attention.

The first task we consider is that of Legal Outcome Prediction. Given the
facts of a case, the task is to predict the outcome of a case Chalkidis et al.
(2019). While both judges and lawyers are interested in estimating the
potential of a case to succeed, they approach this problem from different
angles. As noted above, lawyers are interested in maximising the chance of
winning the case through the arguments they create for their clients. Judges,
on the other hand, are interested in establishing a sound precedent and
serving justice. They base their decisions on the situation of the claimant,
the arguments presented by the lawyers, and their understanding of the law.
The legal NLP models trained for this task do not correspond to either role
above. Instead, both inputs and outputs the models are trained on are
extracted from cases – a transcript of the judge’s reasoning towards the
outcome of a case.

To better understand why is this a problem, let’s first have a closer look at
the model inputs; the facts. When judges describe the facts of a case, they
have already decided on the case outcome. Therefore, the facts used as input
to the models are part of the judge’s argument. A real judge has access to all
the evidence presented by the parties to the court. Legal NLP datasets do not
currently contain this information. Therefore, Legal Outcome Prediction models
decide on the outcome given only a small subset of highly curated data when
compared to a judge. Because the fact selection process introduces clues about
the outcome of a case, it is easier to predict the outcome from this data than
if one had an unbiased full description of what had happened. This renders the
ML operalisation of the task artificially easy.

Furthermore, from a legal standpoint, relying on facts alone is insufficient
because the case decision is never made solely on facts. By relying only on
the facts as an input to the model, the Outcome Prediction Task is an
artificial task, one that would make sense only if we replaced both legal
actors with AI. We have argued in the previous section why this is
undesirable.

Now we can turn to the model outputs; the outcomes. The most popular legal NLP
treatment is to cast the task as a binary decision over all possible laws. The
idea is to predict which laws have been violated, given the facts of a case.
Formally, the model is trained to predict a binary vector $\\{1,0\\}^{K}$,
where $1$ represents a violation of one of the $K$ laws under consideration.
But it is not the case that a judge passes a decision over every law there is.
Instead, a judge makes a decision with respect to a subset of laws – those the
lawyer has alleged as violated. We will refer to these as claims from now on.
If claims are not taken into account, $0$ is left ambiguous as it can
represent either a law that has been claimed as violated but the judge has
decided it is not, or a law that is completely unrelated to the facts at hand.
As Valvoda et al. (2023) point out, the standard operalisation of the task is
therefore artificially easy.

The second task is Similar Case Matching. Under the COLIEE competition Task 1
Rabelo et al. (2020), given a text of a case with redacted references to
previous case law, i.e. the precedent, the task is to correctly predict the
redacted references (but not their location in the original text), see Tab.
1.111111Not all datasets for similar case matching have been sourced this way.
Some, like Xiao et al. (2019), have been manually created and do not fall in
the scope of our critique. While both lawyers and judges search for relevant
precedents, the data used to train the models to find related cases comes
again solely from the judge. However, judges do not select these cases under
some objective metric of relatedness. Instead, a judge cites cases that
support their argument towards the outcome of the case.

This raises two issues. First, one can safely presume that much like the facts
above, the judge-cited cases are an incomplete set of relevant case law. They
almost certainly contain only a portion of the citations that were used by the
two parties to the case. In particular, they are likely to lack some of the
precedent that was relied upon by the party that has lost the case. After all,
if the judge agreed with the losing party’s argument, she would not decide
against it. This means that by relying on the precedent selected by a judge,
the task favours the view of a judge. However, the precedents that the lawyers
have relied upon are equally an indicator of relatedness between any two
cases. These precedents are currently not captured by the COLIEE dataset.

Second, the precedent prediction task is inherently connected to the outcome
prediction task above. If a lawyer wants to claim their client is innocent,
they will be looking at a very different legal argument than if they were to
claim that their client is guilty. By ignoring what role in the argument the
precedent played, a case retrieved by a Similar Case Prediction model can
either support the desired outcome or be against it. From the perspective of a
lawyer, this distinction is crucial when searching for case law to build their
argument around.

Finally, we turn to the task of Legal Question Answering. At first blush,
Legal Question Answering might seem like an emulation of a lawyer. After all,
lawyers are paid to answer legal questions. However, a closer inspection
reveals that the questions in the Legal-Domain Question Answering Dataset for
example Zhong et al. (2020b), are predominantly about the inference of the
crime committed, rather than the explanation of legal concepts. From the
$26,365$ questions in the dataset, $16,604$ are case analysis questions, such
as the one in Tab. 1. The primary difference from the Legal Outcome Prediction
task above is that the facts are stylised and simplified. Take the Legal
Outcome Prediction case from Tab. 1 and compare it to the Legal Question
Answering example below it. The tasks are very similar: in both instances, the
goal is to predict an outcome of a case, given the facts. This is, again, an
automation of the adjudicatory role of a judge.

Legal Outcome Prediction - Chalkidis et al.
---
Facts: _“Ms Ivana Dvořáčková was born in 1981 with Down Syndrome (trisomy 21)
and a damaged heart and lungs. She was in the care of a specialised health
institution in Bratislava. In 1986 she was examined in the Centre of
Paediatric Cardiology in Prague-Motol where it was established that…”_ for
more see: Case of Dvoracek and Dvorackova v. Slovakia
Outcome: Articles: 2, 6
Similar Case Matching - Rabelo et al.
Query: _“The Plaintiff stated that, on the evening of the incident, he was in
the telephone area waiting to use the phone. The assailant jumped the queue in
an attempt to use the phone. The Plaintiff and the assailant "bumped
shoulders"…"_
Precedent: 010, 151
Legal Question Answering - Zhong et al.
Question: _“What crimes did Alice and Bob commit if they transported more than
1.5 million yuan of counterfeit currency from abroad to China?”_
Answer: Smuggling counterfeit money.
Table 1: Examples for three popular legal NLP tasks.

###  7.2 Proposed Solutions

We propose two solutions to the above problems: utilising the information
about legal claims and collecting new datasets of legal briefs. Both claims
and briefs are a product of a lawyer. Therefore, reconstructing the above
tasks around datasets of lawyer-generated text turns them from tasks that
model a judge into tasks that model a lawyer.

Consider how this shift would be achieved by utilising legal claims. For the
Legal Outcome Prediction task and the related Legal Question Answering task,
having the knowledge of both legal claims and outcomes can fix the ambiguity
inherent in the binary classification setting. One could then perform a three-
way classification. The disambiguation is done by subdividing the $0$ category
into negative outcomes and null outcomes. Negative outcomes are the claims
that have not succeeded in the court, null outcomes are unclaimed laws. In
their work, Valvoda et al. (2023) have implemented such three-way
classification and demonstrated significant improvements over previous
approaches.

Alternatively, one could also train the model to predict the claims directly:
claim prediction can be reasonably defined as a binary classification task
Chalkidis et al. (2021). This is a simple solution since claim prediction is
already the focus of LexGLUE ECtHR Task B Chalkidis et al. (2022). Because
claims are the product of a lawyer, the first approach incorporates the
information about a lawyer in the model, while the latter approach models the
role of a lawyer.

Let us now consider how collecting legal briefs could help. Legal briefs are
the arguments the lawyers present to the judge on behalf of their client. They
also contain evidence the argument is based on. A dataset of these briefs
could address the limitations of facts as the sole inputs to the Legal Outcome
Prediction and Legal Question Answering models. Specifically, having access to
briefs would allow for training models conditioned on the full facts of the
case contained in the legal briefs. Better yet, the model outcome could be
conditioned on both the factual description of the situation at hand and the
lawyers arguments. By including the lawyers’ arguments as input to the model,
the outcome prediction task would stop implicitly assuming a fully automatic
legal process, where a judge operates without interaction with a lawyer, but
rather a process with two distinct legal actors; lawyers who are developing
arguments and judges who are evaluating these arguments.

From a practical perspective, a lawyer could use such a model to estimate
their chances of winning a case, given the arguments they develop. As for the
Similar Case Matching task, legal briefs would allow for training models that
consider the full spectrum of precedent relevant to a case. Better yet, with
access to this data, one could begin to develop legal NLP models that have the
ability to find the precedent relevant to a desired outcome of a case.

In conclusion, the tasks described in this section are restrained by the lack
of access to the data produced by a lawyer, which makes them normative to the
view of a judge. The proposed solution is not difficult in practice. Legal
claims are already extracted for the ECtHR dataset, so the shift is a matter
of choice of the task we should prioritise when training our models. While
currently no legal briefs dataset exists, the data to build such a dataset is
publicly available and we are working on collecting and releasing it to the
NLP community.

##  8 Related Work

There are three areas of wider NLP research related to our work. The first
concentrates on the privacy of the data in NLP. The second discusses the
limitations of large language models (LLMs). The third raises concerns about
the ethics of legal NLP. Our work falls in this third category of research.

There is a growing field of research on NLP data privacy Klymenko et al.
(2022). Concerns over privacy naturally arise from training NLP models on
sensitive data, such as medical records Yadav et al. (2016). These concerns
are in particular relevant to legal NLP, since the content of legal documents
is often confidential and might additionally contain medical details. Since
training data can be reconstructed from large language models Carlini et al.
(2021), which underpin much of the current research in NLP and legal NLP, data
privacy is a pressing issue. One approach for privacy-preserving techniques
for legal NLP is the differential privacy approach, a cryptography-based
approach using transformer language models developed by Yin and Habernal
(2022).

Going beyond privacy concerns, machine learning approaches to NLP open the
systems up to ethical questions by the use of human-generated data Hovy and
Spruit (2016). Data from the internet, and from social media in particular,
may contain biases which, if left unchecked, could adversely affect users on
NLP systems trained on such polluted data. These concerns extend to legal NLP
in two ways. First, LLMs harbour biases that can affect legal NLP models built
on top of them. Second, the legal data itself contains biases which we would
not want to be replicated in our models Posner (2008).

Some researchers have conducted studies on uncovering and quantifying
different types of bias Buolamwini and Gebru (2018); Blodgett et al. (2020).
Others have started to develop tools to de-bias the models Ravfogel et al.
(2022); Bolukbasi et al. (2016). With the scale of datasets growing over time,
these issues are likely to grow Bender et al. (2021). Furthermore, the ability
of current LLMs to consider the languages’ social factors is questionable Hovy
and Yang (2021).

Finally, while the law can be viewed as a codification of moral reasoning to
some extent, very little has been written about the ethics of automating the
legal system. There has been an ongoing critique of NLP research aimed at
automating legal sentencing and even calls not to publish this research Leins
et al. (2020). In response to this criticism, others have warned against the
threat of moralism against academic freedoms Tsarapatsanis and Aletras (2021).
In contrast to the previous work, we focus on the roles of the legal actors
and the ethical implications of their automation.

##  9 Conclusion

In this work, we have evaluated the ethical and practical feasibility of
different proposals for NLP research in law. By surveying a number of popular
legal NLP tasks, we identify that the datasets they are built on favour the
voice of the judge. However, because the criteria for what makes a good judge
includes moral discretion and political accountability we believe that
practical applications of these kinds of NLP models face difficult challenges.
For a machine judge to be successful, it would need to have capabilities far
exceeding what is currently available. These capabilities include moral and
social intuition; the sophisticated ability to give explanations; and, in
turn, the ability to receive feedback from members of the public. Even if
these technical challenges were met there are further inherent risks to using
legal NLP in the real world. We have discussed three of them: the
centralisation of power, brittleness and lack of accountability. Comparing the
judge and a lawyer, we find the role of a lawyer less susceptible to these
challenges. Our appeal is, therefore, to focus on the voice of the lawyer in
the legal NLP datasets. Furthermore, we believe that overlooking the role of
the lawyer hinders current neural approaches for modelling law. For the
academic pursuit of furthering legal NLP research, too much focus on the text
produced by judges hides the fruitful and interesting interplay between the
lawyers that fuels the legal discourse.

## Acknowledgements

Research for this publication was conducted as part of the NordASIL project
financed by Nordforsk grant no. 105178 and the Danish National Research
Foundation’s grant no. DNRF169 – Centre of Excellence for Global Mobility Law.

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