Expert Witness Journal Issue 64 December 2025 - Flipbook - Page 88
questions of data protection and con昀椀dentiality
which may limit attempts to broaden the spectrum
of disclosures.
required to use AI-enabled e-discovery can appear
a di昀케cult decision to justify from a time and cost
perspective. Whether clients are willing to pay for
such up-skilling and o昀昀er up their documents to
allow teams to be trained is unclear. Nevertheless,
prompt engineering can be re昀椀ned and improved
by allowing the uploading of small test-batches
of documents to an AI database so as to allow
e-discovery systems to be re昀椀ned. Doubts also
remain as to whether AI tools currently available are
capable of handling matters with a large number of
issues in dispute. For example, if there are numerous
claims for variations within a construction dispute,
it may be that the number of issues exceeds the
capabilities of the platform–with the result that oldfashioned keyword searches may become necessary.
As such, the utility of predictive analytics may be
limited in a world where arbitration maintains
its privacy. Further, the available dataset for AI is
likely to be restricted largely to written material,
thereby omitting a potential wealth of oral and
nonverbal information about an arbitrator–and,
in particular, about what that arbitrator 昀椀nds
persuasive. According to the well-known behavioural
scientist Professor Albert Mehrabian, face-to-face
communication is made up of three main elements:
nonverbal behaviour, tone of voice, and words.
According to Professor Mehrabian,26 words, body
language and tone of voice account for 7%, 55% and
38% of e昀昀ective communication, respectively.
Within construction disputes, however, expert and
professional advisory 昀椀rms have been developing
use-cases for these AI-assisted e-discovery tools,
which are capable of ingesting large amounts of
data. This has been used to develop better ways to
handle the complex and data-heavy claims often
seen in construction projects, including using AI in
the collation of data around delay and disruption,27
with the goal of reducing the time and cost of
document review. Moreover, these seek to use the
plain-language, context-based approach of LLMs
to search on a more holistic basis for evidence that
relates to these claims rather than blunt keyword
searches which may miss a particular nuance in the
document set. For example, just searching for the
word “delay” is not going to pick up an email chain
where parties discuss needing “an extra day”. The
way AI tools review data means that an AI system is
more likely to pick up both types of documents when
昀氀agging for relevance.
Within that context, if AI is only able to review
7% of the available dataset around a person’s
communications, we must conclude that AI is not
seeing the whole picture. The authors therefore do
not consider that AI can supplant the experience of
counsel who have sat face-to-face with an arbitrator
and watched them listen to and evaluate evidence
and submissions in a hearing room. AI is a tool to
help human evaluation, not replace it.
Document Review
Collecting and reviewing documents can be an
expensive and time-consuming exercise, particularly
in complex scienti昀椀c, technology or construction
disputes, where data volumes can be vast. Although
technology-assisted review has been in use in
e-discovery for many years, AI-enabled discovery
tools are a hot topic in the legal world as vendors
release their solutions to the market.
Like lawyers, expert witnesses will be equally
susceptible to the pressure to innovate in order
to maintain relevance and competitiveness. The
experts’ facility with and ability to use AI may
become a key consideration for law 昀椀rms who are
looking to appoint experts on disputes. Those that
are willing to embrace AI in their analysis will
naturally rise to the top.
An obvious question arises as to whether AI can
replace 昀椀rst-level human reviewers, allowing
arbitration teams to focus on the substance of a
dispute rather than the binary decision-making of
whether a document is “relevant” or not. However,
vendors o昀昀ering e-discovery solutions are still
learning about the true utility of AI tools. In
circumstances where e-discovery and AI review
tools remain largely untested by the majority of
practitioners, it may be di昀케cult to justify to many
clients–even those in the construction sector–the
costs associated with licensing and deploying an
AI review tool in circumstances where lawyers will
still need to review the output in any event. This
is because arbitration rules, evidential rules and
ethical rules regulating legal practitioners have not
yet evolved to the point where a human is no longer
required to attest to the nature of a documentary
search undertaken.
Removing the human aspect of any 昀椀rst-level
document review is not without its drawbacks. The
昀椀rst-level review in any e-discovery exercise–even
one which has used a certain level of “machine
learning” within a document-hosting and review
engine–has been an area where junior lawyers within
dispute resolution teams have “cut their teeth” on
large cases. By learning how documents apply to the
pleadings, witness statements and expert reports,
junior lawyers gain the opportunity to understand
how case theory develops and gives them an insight
into the commercial operations of their clients.
Removing junior lawyers’ opportunity to conduct
昀椀rst-level reviews will have consequences for their
development and risks de-skilling them if this aspect
The investment required in up-skilling teams
to be capable of e昀昀ective “prompt engineering”
EXPERT WITNESS JOURNAL
85
DECEMBER/JANUARY 2025-2026
