Expert Witness Journal Issue 64 December 2025 - Flipbook - Page 89
of disclosure is not properly managed. Re-skilling
lawyers so that they can analyse the results of AIassisted e-discovery brackets (i.e., getting humans
to conduct a second-level review) will be important
to ensure that junior lawyers continue to learn
about case theory and how to sift for truly relevant
information.
platforms, now common, are almost all equipped
with AI-assisted transcription capabilities. Subject
to security concerns, being able to generate an AI
generated transcript of a meeting can speed up
the process of generating witness summaries and
proofs of evidence while also ensuring that vital
information is not missed.
Research
Nevertheless, AI transcription remains a work in
progress. It routinely misses or mishears text, and
often contains errors, particularly when speci昀椀c
and unique information is being discussed. AI
transcription is limited only to certain languages
(for instance, the authors have not seen a reliable AI
transcriber that operates in Arabic), requires good
internet connections and good microphones, and
also relies on slow and clear diction. But in a limited
way and at a low cost, they provide a certain level of
accuracy that will allow the reviewer to revisit and
get the gist of a conversation rather than having to
create a note of the meeting from scratch.
AI has a particularly strong use case in legal research,
subject to the risks which are discussed in Section
III, below. Clients with the budgets to access legally
trained AI platforms may be able to dispense with
outside counsel services for some research questions
that they would ordinarily outsource. Being able to
access complex legal analysis in a matter of moments
rather than spending on junior lawyer research
time may give clients enough of a steer to give them
comfort in their decision-making. As such, research
conducted by law 昀椀rms will likely be con昀椀ned to
more nuanced, challenging questions of law which
are not simply de昀椀ned or answered. Given the ability
of a large number of–at least institutional–clients to
conduct their own initial research, 昀椀rms will need
to demonstrate clearly their “value-add” by their
expertise in complex matters.
When coupled with a summarisation tool, it is
possible to create an AI-generated proof of evidence
that can materially streamline the process of
evidence-taking. Indeed, in light of recent criticisms
from the English courts28 about witness statements
failing to comply with English procedural law29 as to
the requirement for a witness statement to refer to
“matters of fact of which the witness has personal
knowledge that are relevant to the case”, there is a
temptation to think that witness statement drawn
from a verbatim AI transcript might be the best way
to ensure procedural compliance. However, as the
English courts have also said, “the best approach for
a judge to adopt in the trial of a commercial case
is, in my view, to place little if any reliance at all on
witnesses’ recollections of what was said in meetings
and conversations”.30
However, as the large–and growing–number
of hallucinated case citations show, there are
substantial risks in clients relying on open-sourced
LLMs as their case-law source. Many law 昀椀rms,
instead, are collaborating with well-respected
industry publishers who have a closed-source LLM
working alongside those industry-publishers’ caselaw databases. With the right coding–and with
specialist lawyers then interrogating the research–
this type of hybrid, or limited AI may prove the
best of both worlds. Law 昀椀rms may still handle
legal research, but when doing so will harness the
power of an LLM to materially reduce the time spent
trawling through case headnotes based on merely
an index or a Boolean search.
In the authors’ experience, memories are seldom
su昀케ciently linear or reliable to allow for a verbatim
transcript of a witness’s recollections to be produced
as a witness statement in a case. While an AI transcript
will help the witness’s voice to be communicated in
an authentic way, achieving an accurate and useable
witness statement will still require a detailed review
of the documents. For the time being, useful and
workable witness statements will still require the
direction of a lawyer to help guide the witness
to focus on relevant facts and documents in a
chronological and thematic way, rather than simply
relying on one person’s ephemeral recollections.
Summarisation and Drafting
AI has proved itself to be very useful in ingesting
large amounts of data, whether that be multiple
documents or long documents, and presenting
summaries of that data which allows for quick
understanding of their contents. This particular
skill has a number of applications for international
arbitration, including condensing long documents
into a short summary that allows lawyers to assess
their relevance, or taking a series of documents
and then sorting and creating a précis of those
documents in a chronology.
Hearings
AI has the potential to revolutionise how hearings
are conducted. Technology now pervades hearings:
electronic bundles, live transcripts and hybrid video
feeds are the norm in arbitration in a post-COVID
world. The next stage is to use AI during a hearing
both to reduce cost and to increase e昀케ciency in
what is an all-consuming stage. AI transcription,
The taking of evidence will also become an AIassisted endeavour. Witness interviews have long
been an exercise in notetaking and remembering
the nuance to be able to craft appropriate and
helpful proofs of evidence. Video conferencing
EXPERT WITNESS JOURNAL
86
DECEMBER/JANUARY 2025-2026
