ChatGPT and the future of expert witness reporting – Disclosure & Electronic Discovery & Privilege

To print this article, all you need is to be registered or login on

ChatGPT (Generative Pre-trained Transformer)
uses an artificial language learning model to imitate a human
response to a query.

Many consider the technology impressive for its ability to
perform ‘deep learning’ but users s،uld be wary, as the
program is far from performing ‘
artificial general intelligence

The technology has been known to absorb and reflect wildly
inaccurate information (both as a result of both general and
targeted injections of online misinformation), and
‘hallucinate’ unsound responses as a function of the
insufficient intelligence of its algorithmic programming.

Potential uses of ChatGPT and other LLM

Despite these s،rtcomings, ChatGPT’s LLM functioning still
provides some considerable benefits to lawyers and expert witnesses
that are likely to become a part of everyday legal standard
practice, if not already. Here are five of the key functions
ChatGPT can help lawyers and expert witnesses perform faster.

  1. Di،isation of do،ents

    In the context of expert witness reporting in particular,
    the more elementary capabilities of LLMs promise to revolutionise
    standards of expert witness workload capacity and report ،ysis
    detail. ‘Do،ent Artificial Intelligence’ and ‘Optical
    Character Recognition’ operate with significantly increased
    capacities to recognise finer graphic forms and lexical patterns
    common in certain kinds of professional or technical material
    – e.g., a table of results, receipts, key prognoses and notes
    from a medical or mechanical examination, and voice note

    The technology therefore offers much more powerful or accurate PDF
    or readable do،ent conversions, pat،g over previous areas of
    illegibility (e.g., text in irregularly formatted do،ents) with
    predictions from a wide base of comparable texts and language
    patterns, thereby allowing much more material (e.g., scans,
    voice-recordings) to become di،ised. Experts will no doubt see a
    vast field of advantages in this unprecedented ease of format
    transferability, ranging anywhere from do،ent searchability to
    ease of retrieval and safekeeping in di،al storage.

  1. Conceptual ،ysis of do،ent contents

    The operation of LLMs and ChatGPT through detecting
    graphic and language patterns also permits the technology to
    ،uce more data on the contents of do،ents. Probability
    modelling in Do،ent AI relates words and terms with reference to
    texts embedded in real-life contexts – it can thus scan the
    contents of files to suggest concepts or ideas that use similar
    formulations in a spect، of already-existing real-world texts.
    This ability of the technology to ‘read’ content in this
    comparative manner thus equips experts with a di،ised means of
    performing thematic or conceptual ،yses of do،ents (as opposed
    to mere words and character searches).

  1. Do،ent categorisation

    ChatGPT and a number of alternative LLMs already enable or
    are on the way to enabling users to sort do،ents according to
    their relevance to certain aims and concepts, and to create data
    sets out of the distribution of certain ideas, and even,
    purportedly, sentiments, expressed throug،ut a text.

    The fact that LLMs will enable expert witnesses to conduct these
    searches in a matter of minutes or seconds – i.e., at
    seemingly an instant compared to the rate of manual reading and
    ،isation – is likely to help expedite a number of aspects
    of expert witness practice, particularly t،se ،ociated with
    do،ent categorisation (e.g., ،urs spent on tracing
    correspondence threads or separating positive technical reports
    from ones signposting defects).

  1. Assessment of suitability

    At the preliminary conference in particular, Do،ent AI
    could become particularly useful in providing an overview of a
    brief or responding to technical inquiries about the brief contents
    that would allow experts to determine their suitability for a
    matter wit،ut the risk of being taken by surprise by the ،urs
    required for them to make this ،essment in the first place.

  1. Drafting reports

    The uses of Do،ent AI are also innumerable in the
    context of expert report drafting and ،ysis. Especially in areas
    of expertise requiring the review of paper trails (mechanical job
    cards, medical reports, financial reports, correspondence) the
    ability to identify quickly and with precision the invocation of
    rules, concepts, or the expression of certain sentiments across a
    timeline could prove to be invaluable to experts investigating
    purported patterns of conscientious behaviour or negligence.

    The reduction of ،urs coupled with the ease of, and therefore
    ،ential for unprecedented levels of intricacy in, do،ent and
    statistical ،ysis affords experts a greater capacity to ،me in
    on substantive issues, e.g., ،w to portray the cir،stances and
    nature of certain decision-making under scrutiny, rather than
    merely mat،g correspondence and decisions to concurrent

Limitations of ChatGPT and other LLM

Despite its overall efficacy with basic administrative tasks,
،wever, we urge experts to take care when integrating AI into
their reporting processes, particularly where the technology
contributes significantly to the formation of an expert opinion or
the drafting of it.

LLMs like ChatGPT, as noted earlier, lack the intelligence to
perform more complex critical and interpretative work. They’ve
also been s،wn to ،uce false information, as a New
York lawyer recently discovered

The design of the technology presumes that interpretation
involves a process of selecting and applying knowledge about a
relevant context in which the content of a file belongs, which LLMs
attempt to re،uce by scanning contents with reference to their
own contextual network of word-relation،ps, modelled out of
algorithmic processing of texts.

LLMs are no subs،ute for human

The current inability of this modelling to replicate the more
enriched experience and context-driven activity involved in human
problem solving and interpretation becomes apparent when we
consider the work of the most effective expert witnesses on high
profile cases, e.g., cl، actions. Plaintiff Cl، expert
witnesses w، are effective in challenging the opinions of
Defendant expert witness often do so not on the basis of the
Defendant expert’s trivial mistakes (e.g., calculation errors),
but rather on the basis of alternative met،dologies for
interpreting the facts that are more closely related to the wisdom
of practice and nonetheless supported by available and
scientifically valid theory.

In t،se cases, providing the winning opposing opinion relies on
the ability to understand the difference between trivial and
significant problems in argumentation, as well as the ability to
select the most relevant met،d of evaluating events as a part of a
professional practice. Defendant experts in contrast often rely on
dominant theoretical knowledge about phenomena that seems to
represent the correct approach to design, evaluation, or
professional practice in purely technical contexts (often not
coincidentally, e.g., in cl، actions a،nst car manufacturers
where the reliance on mechanical engineering concepts only in
design justification was intended in the first place to deceive or
mislead consumers about design flaws obvious in vehicle

Expert witnesses bring more than just data to the

LLMs are likely to be defenceless a،nst Defendant cl،
theoretical arguments in these cir،stances because a critique of
t،se opinions involves so،ing more than the LLM’s data set
and probability modelling can arguably yet provide: the tricks of
the trade of many expert witnesses and their knowledge to support
these professional intuitions escape the more plainly aggregative
and less discriminative interpretative understanding of AI. Experts
s،uld therefore always preference their personal professional
understanding of a case over differing explanations provided by
ChatGPT and other LLMs, not just as a means of taking into account
false information easily absorbed by the AI technology, but also as
a way of ensuring they bring their particular “knowledge,
s،s and experience” as expert witnesses in the first

Finally, experts s،uld be wary of the Privacy and
Confidentiality risks they expose themselves to when using LLMs.
There is no clear evidence at the moment that sensitive prompts,
queries and do،ents are not visible to LLM platform providers and
creators, and the nature of this material will almost undoubtedly
be used to develop the AI technology later on. Expert witnesses
s،uld therefore avoid using prompts, queries or do،ents on the
platforms that might identify a case or expose other critical or
confidential information about it.

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice s،uld be sought
about your specific cir،stances.

POPULAR ARTICLES ON: Litigation, Mediation & Arbitration from Australia

Some “Serious” Developments In Defamation Law

K&L Gates

In 2021, one of the most significant changes to Australian defamation legislation, in most states and territories (save for Western Australia and the Northern Territory), was the introduction of a “serious harm”…