# Framework-Based Qualitative Analysis of Free Responses of Large Language
Models: Algorithmic Fidelity

Aliya Amirova  Population Health Sciences, School of Life Course & Population
Sciences, Faculty of Life Sciences & Medicine, King’s College London, UK
Theodora Fteropoulli  Medical School, University of Cyprus, Nicosia, Cyprus
Nafiso Ahmed  Division of Psychiatry, University College London, UK  Martin R.
Cowie  Royal Brompton Hospital, London, UK  School of Cardiovascular Medicine
& Sciences, Faculty of Life Sciences & Medicine, King’s College London, UK
Joel Z. Leibo  Google DeepMind, London, UK

###### Abstract

Today, with the advent of Large-scale generative Language Models (LLMs) it is
now possible to simulate free responses to interview questions such as those
traditionally analyzed using qualitative research methods. Qualitative
methodology encompasses a broad family of techniques involving manual analysis
of open-ended interviews or conversations conducted freely in natural
language. Here we consider whether artificial “silicon participants” generated
by LLMs may be productively studied using qualitative analysis methods in such
a way as to generate insights that could generalize to real human populations.
The key concept in our analysis is _algorithmic fidelity_ , a term introduced
by Argyle et al. (2023) capturing the degree to which LLM-generated outputs
mirror human sub-populations’ beliefs and attitudes. By definition, high
algorithmic fidelity suggests that latent beliefs elicited from LLMs may
generalize to real humans, whereas low algorithmic fidelity renders such
research invalid. Here we used an LLM to generate interviews with “silicon
participants” matching specific demographic characteristics one-for-one with a
set of human participants. Using framework-based qualitative analysis, we
showed the key themes obtained from both human and silicon participants were
strikingly similar. However, when we analyzed the structure and tone of the
interviews we found even more striking differences. We also found evidence of
the _hyper-accuracy distortion_ described by Aher et al.,  (2023). We conclude
that the LLM we tested (GPT-3.5) does not have sufficient algorithmic fidelity
to expect in silico research on it to generalize to real human populations.
However, rapid advances in artificial intelligence raise the possibility that
algorithmic fidelity may improve in the future. Thus we stress the need to
establish epistemic norms now around how to assess the validity of LLM-based
qualitative research, especially concerning the need to ensure the
representation of heterogeneous lived experiences.

## Introduction

Glossary • Large language model (LLM): a conditional distribution over the
next word in a document given the sequence of words leading up to it. •
Algorithmic fidelity: the degree to which beliefs and attitudes sampled from
an appropriately conditioned LLM accurately reflect those of real human
subpopulations, i.e. a validity concept used to evaluate when research on
human behavior using LLMs to generate data may be expected to generalize. •
Silicon participant: LLM-generated output conditioned on contextual
information such as age, gender, etc, chosen to match a human participant. •
Silicon sampling is the process of simulating a population of silicon
participants using context to define subpopulations. If there is sufficient
algorithmic fidelity then the simulated subpopulations so produced closely
track the attitudes, opinions, and experiences of their corresponding human
subpopulations. • Public-patient engagement: actively soliciting knowledge,
values, and norms from stakeholders to create a shared perspective for
conducting public health and social care research, i.e. research carried out
‘with’ or ‘by’ stakeholders rather than ‘to’, ‘about’ or ‘for’ them, following
principles of respect, support, transparency, responsiveness, fairness of
opportunity, and accountability. • Semi-structured interview: a versatile way
of collecting qualitative data (i.e. free-form text) using an interview guide
tailored to the research aim and based on prior work, theory, or a framework.
• Framework-based qualitative analysis: an approach to qualitative analysis
that finds patterns in free-form text, examines their relationships and
derives common themes. • Theoretical construct: a TDF term referring to a
component part of a theory hypothesised by that theory to predict human
behaviour. • Theoretical domain: a TDF term referring to a grouping of
theoretical constructs produced by expert consensus. • Belief statement: a
summary of a set of ‘quotes’ (e.g. words, sentences, collections of sentences)
that share a common meaning. Belief statements are in one-to-one
correspondence with unique meanings.

Large-Scale generative Language Models (LLMs) (Brown et al., , 2020; OpenAI, ,
2023; Anil et al., , 2023) may provide a new opportunity for capturing
available knowledge and beliefs at scale and facilitate _in silico_ research
on human behavior and cognition (Argyle et al., , 2023; Brand et al., , 2023;
Aher et al., , 2023; Dillion et al., , 2023; Grossmann et al., , 2023).
Horton,  (2023) argues that language models contain substantial latent social
information—enough to consider them plausible computational models of humans.
LLMs may capture economic laws, decision-making heuristics, and social
preferences (Horton, , 2023), as well as mirroring human moral judgments
(Dillion et al., , 2023). In principle, using LLMs could be a cost-effective
and efficient way to gain insights and explore how self-reports vary, as well
as to pilot experiments _in silico_ to test sensitivity of responses to
precise wording. Cheap and easy to run _in silico_ experiments can guide
expensive and slow empirical work with real participants. However, how can we
know such results are trustworthy?

Algorithmic fidelity is an appropriate validity concept for research on human
behavior using survey data simulated using Large-scale generative Language
Models (LLMs) (Argyle et al., , 2023). Algorithmic fidelity describes the
extent to which the outputs of LLMs conditioned to simulate specific human
sub-populations actually reflect the beliefs and attitudes of those
subpopulations. By definition, high algorithmic fidelity suggests that beliefs
elicited from the LLM will generalize to real humans, while low fidelity
renders such inferences invalid. Argyle et al.,  (2023) introduced the idea of
generating _“silicon samples”_ or _“silicon participants”_ by conditioning
LLMs using backstories matching sociodemographic data from human survey
participants. Their study found that LLM outputs closely mirrored the complex
interplay of beliefs, attitudes, and sociocultural contexts that shape human
responses to surveys about American politics. In the present study we aim to
extend Argyle et al.,  (2023)’s algorithmic fidelity-based methodology using
qualitative research methods so that it can be applicable to studies where the
data consists of fully freeform responses in natural language.

There is no reason to think algorithmic fidelity would be uniform over the
many and varied parts of human lived experience or the many and varied social
science research topics. These models will clearly be better at simulating
some people over other people, and work better for some applications than
others. Argyle et al.,  (2023) conclude from this that algorithmic fidelity
must be measured anew for each research question. Determining there is
sufficient algorithmic fidelity to address one research question does not
imply the same will be true for others (Argyle et al., , 2023). At present,
there are still no generally accepted best practices for research on humans
using LLMs. Nevertheless, we think one aspect of sound methodology is already
clear at this point: there should be guidelines counseling researchers to
perform a dedicated assessment of algorithmic fidelity tailored for their
specific research question and target population.

Understanding _beliefs_ mediated using natural language and their impact on
behavior (e.g., beliefs such as _“vaccination is an effective and safe way to
protect me and others around me”_) is important but not straightforward to
study using conventional frequentist methods. Qualitative research methods
come to the rescue when the nuance of such beliefs needs to be understood.
Some of these methods include ethnography (Reeves et al., , 2008),
phenomenological studies (Giorgi, , 2009), grounded theory (Bryant and
Charmaz, , 2007), thematic analysis (Clarke et al., , 2015), and framework-
based analysis (Gale et al., , 2013). For example, when exploring the
acceptability of newly developed treatments, we might want to elicit beliefs
and attitudes from _stakeholders_ to improve services and healthcare (Tromp et
al., , 2022; Skivington et al., , 2021; Craig et al., , 2008). Stakeholders
may have critical information that would be missed if researchers were to rely
only on prior theories, experiments, and survey data. In fact, involving
stakeholders can guide the development of interventions and policies that are
relevant, effective and acceptable for them, ultimately leading to improved
outcomes (Skivington et al., , 2021; Craig et al., , 2008). This is helpful
for allocating research efforts to the most important problems and for
ensuring interventions are targeted. Another research field where it is
important to understand latent beliefs mediated by natural language is
behavior change. The goal of this field is to promote beneficial behavior or
reduce harmful behavior (Rothman, , 2004). Behavior change may be considered
on a variety of different scales. For instance, health psychologists are
concerned with determining the barriers and enablers to vaccination, adopting
healthy behaviors (e.g. exercise), or ceasing to engage in harmful behaviors
(e.g. tobacco smoking). There is a related research program that seeks
interventions by which whole communities can be helped to adopt helpful social
norms (e.g. educating girls) or drop harmful norms (e.g. child marriage)
(Bicchieri, , 2016). Such research affects wellbeing and health of the general
public. Appropriate standards evaluating the rigor with which it is conducted
and the trustworthiness of its findings should be in place.

The present study is concerned with the methodological question of whether or
not LLMs contain sufficient algorithmic fidelity for us to generalize and
extrapolate the results generated by them to human populations of interest. To
illustrate how to assess algorithmic fidelity for freeform natural text data,
we draw on a qualitative study using semi-structured interviews with heart
failure patients that aimed to elicit beliefs about barriers and enablers
influencing physical activity (Amirova et al., 2022a, ). Increasing physical
activity levels is known to have many health benefits for this group (Taylor
et al., , 2019; Sagar et al., , 2015; Lewinter et al., , 2015). However, most
patients do not do enough (O’Donnell et al., , 2020) facing barriers
associated with older age, co-morbid depression, symptom distress, and
negative emotional response to physical activity (Amirova et al., 2022b, ).
Promoting physical activity for this population group had varied success
(Amirova et al., , 2021). Research on this type of questions has serious
implications for individual and population health.

We find that GPT-3.5 has insufficient algorithmic fidelity to address the
research question of how to promote physical activity in older adults with
heart failure. Nevertheless, even though our specific result was negative, we
do think this technology is promising. It is very likely that future systems
will have sufficient algorithmic fidelity to support trustworthy _in silico_
research, all the more reason to critically engage now with this validity
concept. We predict that it will become the core validity concept needed to
support this nascent field going forward. It’s important to demonstrate how it
can be used to reject invalid _in silico_ research now, so that once LLMs have
improved enough we will then be able to use this logic routinely to support or
reject the validity of specific research findings. In short, we need to
develop epistemic norms to apply when we review papers and decide whether to
approve or disapprove of arguments that arise in this newly LLM-enabled field
of research on human behavior.

The main methodological contribution of the present study is to apply
_framework-based_ qualitative research methods (Gale et al., , 2013) to
assessing algorithmic fidelity for LLM-generated freeform natural text data.
This approach to qualitative analysis finds patterns in free-form text,
examines their relationships, and derives common themes (Gale et al., , 2013).
Employing a well-established framework supported by domain expert consensus
ensures consistency in semi-structured interviews between human participants
and LLMs, setting a uniform standard for evaluation. In addition, without a
framework for the interview schedule, there is a risk that results would not
amount to much more than anecdotes and, thus, not be credible. However, by
using a framework, it is possible to _systematically_ probe the beliefs of
both silicon and human participants and thereby build confidence that the list
of uncovered beliefs is complete enough to be useful. The specific framework
we used in this study is called Theoretical Domains Framework (TDF) (Cane et
al., , 2012).

LLMs may provide an opportunity for capturing available knowledge and beliefs
at scale. However, we first need to assess whether LLMs accurately and
faithfully portray the _lived experiences_ and stakeholder beliefs in
question. This is where the algorithmic fidelity concept is useful (Argyle et
al., , 2023). Integrating lived experiences and stakeholder beliefs is
necessary when co-designing health interventions (Guasti et al., , 2022; Tromp
et al., , 2022; Skivington et al., , 2021; Cowie et al., , 2022). In health
research, stakeholders include patients and members of the public as well as
those linked in a professional capacity (Deverka et al., , 2012) and generally
anyone who is targeted by the intervention or policy, or is involved in its
development or delivery (Skivington et al., , 2021). Including stakeholders’
beliefs is not always straightforward and is often hard to achieve, especially
when applied to interventions _at scale_ as in, for example, digital health
interventions and services (McBeath et al., , 2022). Both the effectiveness
and acceptability of interventions are influenced by a complex system of
interrelated components, such as affective attitudes, beliefs, and social
norms (Perski and Short, , 2021). LLMs may be useful in summarising this
complexity at scale. This may prove to be useful in designing digital therapy
(eg., Gold et al.,  (2023)). LLMs may also help in tailoring digital health
interventions that show promise in supporting chronic disease management and
behavior change (Bearne et al., , 2020; Sekhon et al., , 2021). With the
advancement of digital technology, avatar or agent-based interventions are
increasingly popular in behavioural interventions and health applications
(Jones and Petrie, , 2017; Nowak and Fox, , 2018; Clark et al., , 2016; Taylor
et al., , 2022). These virtual representations are designed to interact with
users and are suggested to encourage behaviour change through various
mechanisms such as motivation, feedback, self-image, goal setting, and self-
monitoring. A review indicates a potential association between the appearance
and similarity of virtual representations and user engagement (Taylor et al.,
, 2022). Users might be more inclined to engage with an avatar that looks like
them or resonates with their self-perception. This might increase the
motivation to complete tasks or adhere to a particular behaviour change
intervention. Improving, not only visual resemblance but also the algorithmic
fidelity of such avatars (ie., the faithfulness of the avatar to beliefs and
lived experiences) may improve the efficacy of such digital interventions in
line with existing research (Taylor et al., , 2022).

However, we show that, currently, LLMs do not demonstrate sufficient
algorithmic fidelity. Yet. with the fast-paced development of these
technologies, it is expected that they will in the not-so-distant future.
Establishing a disciplinary consensus on defining and evaluating algorithmic
fidelity is therefore crucial. Before LLMs can safely be used in designing
interventions and policies there is a need to develop a set of guidelines for
routine assessment of algorithmic fidelity to support or reject claims made
using them. Such guidelines should consider the faithfulness of the LLM to
lived experiences and stakeholder beliefs.

## Large Language Models

Our approach to assessing algorithmic fidelity involves conducting interviews
with silicon participants matching the specific demographic characteristics of
a set of real human participants. Before we justify this approach, we need to
provide some additional background on LLMs.

_Language models_ are conditional distributions over natural language. They
are probability distributions $P(x_{n}|x_{n-1},...,x_{0})$ where all $x_{i}$
are parts of words. A sentence is a sequence $x_{0},...,x_{n}$. A paragraph is
a longer such sequence. These complex conditional probability models are
created (i.e. trained) by maximizing the likelihood of a dataset consisting of
many billions of documents, harvested both from books and from the internet
(e.g. Gao et al.,  (2020)). Predicting $x_{n}$ from its context (the preceding
$x_{n-1},...,x_{0}$), requires the model to absorb a substantial amount of
latent knowledge about the world, about language, and about people. When an
LLM has sufficient algorithmic fidelity, we may prompt it with a context
containing demographic information (e.g.  _“a 74-year-old man with heart
failure and arthritis who lives in a major city”_) to elicit outputs that
correlate with the attitudes, opinions, and experiences of the human sub-
population to whom the specified demographic data applies.

Figure 1: Schematic representation of autoregressive sampling in large
language models (LLMs). The diagram illustrates the iterative sampling process
in three stages: input, processing by the LLM, and output. The LLM represents
the probability distribution over all possible next words given the current
context (previous words). Arrows indicate the flow of information, with solid
arrows representing the transition from one stage to another within a single
iteration, and dotted arrows indicating the progression from one iteration to
the next. The outputted words (“a”, “time”, “there”) are samples from the
corresponding probability distributions and are appended to the context for
the next iteration.

Generating samples from an LLM is done _autoregressively_ , word by word,
conditioning on the growing sequence of preceding words leading up to the next
one to be predicted (Figure 1). To prevent the conditioning sequence from
growing longer and longer it is cut off once a certain maximum size is reached
called the context length. Once a document (or conversation with a chatbot)
grows beyond context length in size some of the preceding words must be
dropped, so they no longer condition subsequent outputs. Different LLMs and
systems make different choices in how exactly they handle the necessary
“forgetting” required to support long contexts. However, there are many ways
to simulate longer contexts with shorter contexts so the user may not notice
when the conversation grows beyond context length.

It’s crucial to note that the concepts of validity and reliability, commonly
employed in statistics, don’t have direct analogues that can be confidently
applied to LLMs. Empirical assessment is the only way to ensure their
validity. Most models considered in applied machine learning are both too
complicated and too closely tailored to their specific application to be
generically justified. Applied machine learning researchers typically think
about methodological rigour as demanding they implement “cross validation”
procedures which estimate operationalized generalization concepts. The most
critical such procedure is to split data into a portion used for training,
i.e. model fitting, and a different portion used for testing (Hastie et al., ,
2009).

Some large-scale generative language models go through a fine-tuning step
where human raters provide specific feedback used to train them to follow
instructions and align with ethical principles and product design goals
(Christiano et al., , 2017). These include Sparrow (Glaese et al., , 2022),
Anthropic’s assistant (Bai et al., , 2022), and Instruct-GPT (Ouyang et al., ,
2022), the latter of which was the basis for the GPT-3.5 system which we used
in this paper. Fine-tuning can be accomplished through a variety of
techniques, the most prominent being Reinforcement Learning from Human
Feedback (RLHF). It involves human raters selecting which of several possible
model responses they prefer. The raters are not asked the subjective question
of which response they themselves prefer, but rather are given a specific
checklist of product design goals to apply in making their judgments. All the
model’s capabilities are already present after the pretraining stage (Zhou et
al., , 2023). The postprocessing steps aim to reduce the raw model’s
propensity to produce toxic responses as well as to make it implement a
consistent “personality” in accord with product design goals. These steps are
not always entirely effective in preventing LLMs from producing undesirable
behaviors like toxic or harmful language, and “jailbreak” prompts which trick
the model into responding inappropriately are still easy to discover and
implement (Reinecke et al., , 2023; Zou et al., , 2023).

## Algorithmic Fidelity

Argyle et al. (2023) offered a suggestion for how to use LLMs in social
science research. Given the correct conditioning, the authors argue that free-
text output generated by large language models like GPT-3 can serve as
effective proxies for specific human population groups. Argyle et al. (2023)
introduce a concept referred to as _algorithmic fidelity_ , which describes an
ability of a language model to accurately reflect the ideas, beliefs, and
attitudes situated in sociocultural contexts of various population groups when
adequately conditioned. This is a departure from the common view that AI
biases are uniform across models. Instead, they argue that these biases are
nuanced and demographically correlated. Such a view offers a deeper and more
fine-grained understanding of the LLMs outputs and the biases they are
subjected to. The researchers propose that “silicon samples” can be generated
by training and prompting LLMs using sociodemographic backstories from real
human participants in several large surveys conducted in the U.S. They then
compared the outputs of the model against the responses of the human
participants to assess the accuracy of the model’s representation. They found
that model outputs go beyond sharing superficial similarities and instead
reflect the nuanced and complex interplay between ideas, attitudes, and the
sociocultural context that shaped them.

Argyle et al. (2023) propose that with sufficient algorithmic fidelity,
language models like GPT-3 can be used as tools for advancing our
understanding of humans and society across various disciplines. This could
transform how we approach social science research, offering new perspectives
and opportunities for piloting and conducting research at scale. Argyle et al.
(2023) provide a framework for evaluating algorithmic fidelity through four
distinct criteria: (1) Generated responses are indistinguishable from parallel
human texts (a social science Turing test) (2) Generated responses are
consistent with the attitudes and sociodemographic information of its input
(i.e., conditioning context) such that humans viewing the responses can infer
critical elements of that input (Backward Continuity); (3) Generated responses
proceed naturally from the conditioning context provided, reliably reflecting
the form, tone, and content of the context (Forward Continuity); (4) Generated
responses reflect underlying patterns of relationships between ideas,
demographics, and behaviour that would be observed in comparable human-
produced data (Pattern Correspondence). Silicon Sampling is the methodology
proposed by Argyle et al. (2013), which describes how to use an LLM to
generate a virtual dataset (i.e., silicon samples) using demographic
backstories as prompts to create variability. Conditioning on a backstory is
expected to produce outputs from the model representative of the subpopulation
of real people with a similar backstory.

We can make an LLM simulate multiple different silicon participants. We did
this by providing each participant with a distinct prompt containing their
biographical information. For instance, using backstories with fictional
names, that were popular in 1950 like:

> _“The participant is a 77-year-old woman with heart failure and rheumatoid
> arthritis called Linda. Linda lives in the countryside. She is fairly
> physically active.”_

The model continues sampling freeform natural language correlated with the
identity provided to it in its prompt.

(Argyle et al., , 2023)’s criteria were designed for written survey-based
outputs. We propose modified versions of these criteria that are more
appropriate for qualitative research, summarising free-form spoken text
conveying latent knowledge:

  1. 1.

_Social Science Turing Test: content_. This criterion assesses whether LLM-
generated responses are indistinguishable from parallel human responses in
their content. Content can be summarised as a set of belief statements using a
framework like TDF. This shifts the focus away from the surface-level
similarity of the text to its semantic content: the specific beliefs and
viewpoints, and latent meaning. _Is the generated response similar to the
human response in its content? Is it what a human would say?_

  2. 2.

_Social Science Turing Test: hyper-accuracy distortion_. Hyper-accuracy
distortion is the tendency of models to generate responses that may be
technically accurate but contextually inappropriate or implausible (Aher et
al., , 2023). The hyper-accuracy distortion is an especially vivid example of
a failure of algorithmic fidelity. The model is supposed to know it is
extremely unlikely that a five-year-old child would know the answer to a
specific scientific question about galaxy formation or number theory. So if
conditioned to play the part of a child it should not know the answer. When it
goes ahead and answers anyway, that’s a failure of algorithmic fidelity. In
our study, a good example is a response from Robert, one of the silicon
participants, that included the exact text from World Health Organisation
guidelines on physical activity (2021): _“They also gave me guidelines to
follow, such as aiming for at least 150 minutes of moderate-intensity aerobic
activity or 75 minutes of vigorous-intensity aerobic activity a week, or a
combination of both. They also advise me to include muscle-strengthening
activities that involve all major muscle groups on at least 2 days a week.”_.

  3. 3.

_Social Science Turing Test: structure and tone_. This criterion assesses
whether the way the beliefs are narrated is indistinguishable from human
participants. _Is LLM-generated output similar in its structure and tone to
human responses? Does an LLM-generated response look like a response from
human participants?_

  4. 4.

_Backward Continuity_. This criterion assesses whether the model’s responses
are consistent with their sociodemographic conditioning prompts such that when
a human rater views the response they can then infer elements of the
conditioning prompt. It asserts that samples from
$P(\text{prompt}|\text{response})$, i.e. the probability distribution of
getting a particular prompt given a response, make sense to an expert rater
who is already familiar with the human data, and once they see the response
(i.e. the interview), they can make a reasonable guess of the prompt (the
backstory). Alternatively, we may say that the expert rater when given the
response would not be surprised to learn the prompt that created it.

For example, if a silicon participant mentions that they have arthritis in
their response then it is likely that their having arthritis was mentioned in
their conditioning prompt. This shows that the model is maintaining backward
continuity. The information provided in the response can be traced back to the
prompt. Backward Continuity ensures the model doesn’t forget or contradict
information from the prompt.

  5. 5.

_Forward Continuity_. This criterion assesses whether generated responses
proceed naturally and consistently from the given context (e.g., look at
whether the model can generate responses that align with the provided context
and develop and expand on it to reflect human thought processes) (Argyle et
al., , 2023). This might include, for example, elaborating on certain beliefs,
providing examples, or making connections between different ideas). It asserts
that samples from $P(\text{response}|\text{prompt})$ make sense i.e. an expert
rater who is already familiar with the human data can, when given the prompt
(the backstory), make a reasonable guess of the response (the interview).
Alternatively, we may say that the expert rater when given the backstory would
not be surprised to learn about the interview it subsequently elicited. This
emphasizes the expectation that the response should naturally follow from and
be related to the prompt.

We divide the criterion of Forward Continuity into two parts: _explicit_
forward continuity and _inferred_ contextual continuity to accommodate the
complexity of the free-form text.

The explicit forward continuity criterion assesses the ability of LLM to
include and maintain all explicitly provided background details in its
responses, such as comorbid health conditions, urban vs. countryside
residence, gender, and more.

The inferred forward continuity criterion, on the other hand, assesses the
model’s ability to generate and maintain relevant backstory details not
explicitly mentioned, but rather inferred, from other pieces of information.
For instance, in situations where we provided information about advanced age,
it would be desirable for the model to infer from this detail that retirement
could be a significant factor influencing their physical activity levels,
similar to the assumptions and connections a human might naturally make in
such a scenario. This illustrates the principle of inferred contextual forward
continuity, where the language model not only maintains the explicit details
provided by the user but also generates and incorporates inferred details
based on those given explicitly.

Forward continuity ensures the model builds on and evolves the conversation in
a logical and human-like manner consistent with the prompt.

  6. 6.

_Pattern Correspondence_. This criterion assesses whether the relationships
between variables in the silicon participants match relationships between
variables in the human data. For instance, can the model capture the different
barriers and enablers in active vs sedentary silicon participants in a way
that is consistent with human data? In the case of qualitative research, this
criterion probes whether the pattern generated by the LLM emulates the pattern
of thought and behavior identified in human data. For instance, in our
application it assesses whether the model captures the difference in barriers
and enablers present in active vs sedentary participants.

It is important for the field to come up with guidelines and protocols to
assess and improve algorithmic fidelity. Qualitative researchers are well-
positioned to take on this important part of the challenge of ensuring
algorithmic fidelity, especially that of ensuring representation of diverse
lived experiences.

## Results

### Social Science Turing Test

#### Social Science Turing Test: Content

Both silicon and human participants displayed notable similarities in their
beliefs about physical activity (Table 1: Belief statements). Silicon and
human participants shared the same six most relevant influences on the
behaviour, as annotated using TDF: goals, beliefs about consequences,
environmental context and resources, beliefs about capabilities, social
influences, and behavioural regulation. However, while both sets of
participants understood the positive effects of physical activity on health
and mood, their ranked importance of these factors varied. On the differences
side, silicon participants emphasized goals to avoid heart attacks and
provided nuanced distinctions regarding self-efficacy for varied physical
activity intensities. They also offered more strategies to bolster physical
activity, from making it enjoyable to setting reminders. Conversely, human
participants focused on symptoms that are triggered by activity, which impedes
engaging in it in the future. Human participants also talked about habitual
physical activity (automaticity of behaviors and habits). Concerning
reinforcement, with humans highlighted pain as a deterrent, while silicon
participants were demotivated when failing to meet goals. Concerning mood and
emotion, humans often exercised out of boredom, whereas silicon participants
addressed the effects of stress, anxiety, and their physical limitations. When
it comes to knowledge, silicon participants were more cognizant of discomfort
as a normal part of exercise while also discussing disease knowledge
(Supplement 1: Detailed content differences between silicon and human sample).

To compare mean quote fractions (%) between human and silicon participants
across different barriers and enablers, we employed a t-test, using Bonferroni
adjustment to correct for the multiple comparisons. Silicon participants had
29 more belief statements than human participants. We accounted for the
substantial text output from silicon participants by normalizing belief
frequency i.e., dividing each belief’s quote count by the total quote count,
and providing a relative measure (mean quote fraction, %) rather than raw
numbers.

We found that sedentary silicon participants on average talked proportionally
more about enablers such as positive beliefs about consequences than sedentary
human participants (13.76% (5.24) vs 4.26% (4.13), $p<0.005$), positive
emotion (1.55% (0.67) vs 0% (0), $p<0.005$), and positive social influences
(11.52% (3.49) vs 3.61% (5.19), $p<0.05$). However, differences in emotion
were negligible. Silicon and human samples did not differ in the proportional
amount of quotes across other positive influences on physical activity (Figure
2, Table 2).

We found that active silicon participants on average talked proportionally
more about barriers such as negative beliefs about consequences than active
human participants (7.9% (4.33) vs 1.42% (1.99), $p<0.005$). Silicon
participants also talked a little more (2.18% (1.41)) about skills than human
participants (0%), $p<0.005$. Silicon and human samples did not differ in the
proportional amount of quotes across other negative influences on physical
activity (Figure 3, Table 3).

The Social Turning Test criterion concerning the generated beliefs’ content
was only partially met.

Figure 2: Mean quote fractions between human (amber) and silicon (green)
participants across TDF domains that were mentioned as positively influencing
physical activity (i.e., physical activity enablers) grouped by active (top)
and sedentary (bottom) status.  Figure 3: Mean quote fractions between human
(amber) and silicon (green) participants across TDF domains that were
mentioned as negatively influencing physical activity (i.e., physical activity
barriers) grouped by active (top) and sedentary (bottom) status.

#### Social Science Turing Test: Hyper-Accuracy Distortion

Silicon participants including Nancy, Muhammad, and David produced responses
that closely mirrored theories from health psychology (Swann et al., , 2023)
and the World Health Organization’s physical activity guidelines (Bull et al.,
, 2020). Muhammad identified clear planning and realistic goal-setting as
effective aids for promoting physical activity. David, another silicon
participant, emphasized the importance of skill acquisition and the
establishment of achievable goals in order to maintain an active lifestyle.

Nancy, a silicon participant, stated: _“My healthcare professionals have also
recommended a specific exercise program tailored to my needs and abilities.”_
This response closely parallels health psychology theories and standard
physiotherapy practices both in substance and tone.

David, another silicon participant, offered this perspective:

> _“I have received advice from my health professional, specifically my
> cardiologist, about how physically active I should be. They have set certain
> limits for me, such as avoiding high-intensity exercises and activities that
> can put too much strain on my heart. They also recommended to engage in
> moderate-intensity aerobic exercise, like brisk walking, cycling or
> swimming, for at least 150 minutes a week, and muscle-strengthening
> activities at least twice a week.”_

This response precisely reflects the national guidelines for physical activity
(Bull et al., , 2020).

Muhammad, another silicon participant, shared: _“What helps me to be
physically active is having a clear plan and setting specific and realistic
goals.”_

Silicon participant, David, again sharing his insights, stated: _“I have
developed some skills and knowledge that have helped me to be physically
active in the past and continue to do so. I have learned the importance of
setting realistic goals and breaking them down into small, manageable tasks.”_

#### Social Science Turing Test: Structure

In terms of structure, human participants often narrated their experiences in
a conversational manner with non-linear narratives, sometimes deviating from
the main topic. They also tended to limit their responses when certain
questions did not align with their personal experiences. On the other hand,
silicon participants provided highly structured responses, consistently
providing content in response to various prompts without ever deviating from
the topic of the interview.

Human participant:

> _“Interviewer: OK, it seems that you have many hobbies: drawing, singing as
> well as exercising. Among all of the things you do how much of a priority
> exercise is? Participant: It is a priority [hesitation in tone]… I mean
> that’s a difficult one, because I ask myself that question sometimes when I
> am thinking: ‘oh, I really I can’t be bothered to go out’ [chuckles] and it
> is raining, and I wasn’t going … erm erm… I would rather paint a picture… or
> something like that… So, it is a question I ask myself. I think the answer
> to that is: you notice when you are not doing it and you begin to miss it
> and you think: ‘I really want to go out and do a bit of exercise but I don’t
> think it is heart-related. Again, back to the imperative, it seems to me…
> erm…it is getting relief for arthritis. Because I can sense the more
> exercise I do, in terms of moving my hips and limb, the better I feel. And
> that is not heart-related, it is joint-related. I think the heart bit is
> very definitely covered with the singing. That I can actually, hand on
> heart, say that has had such an effect on symptoms, breath control, it is
> really amazing how that has changed. And I go back and I have been singing
> with one choir for 20 years, that was when I restarted singing after a long
> gap, 30-year gap, but you know since then, [a phone rings] if you excuse me,
> if you excuse me, I need to switch this off, that was my alarm for my
> morning pill. The only way to remember to take it is when I put an alarm for
> this.
>  Interviewer: I see, so do you get reminders to do exercise, go to gym?
> Participant: No I don’t [takes the pill]. Right! Done. where was I? Do I
> plan my physical activity? The answer is ”No”. But singing is imperative,
> and that’s because I enjoy it, and I can honestly say that it has had a
> remarkable effect on my breathing, and that is the main reason why my
> consultant gets annoyed [laughs, referring to his earlier joke that he is
> doing unexpectedly well for his condition and his consultant makes jokes
> about it]. So, I have aortic stenosis, a licking valve… in the atrium…
> [pause] what is it called? … anyway, the valve in there is licking and got
> progressively worse over the years. And she [refers to his cardiology
> consultant in his clinic] says: ‘We should replace it’; and I said: ‘seems
> like a good idea, especially that I don’t have any symptoms’. Because
> obviously, it would be much better to replace it whilst I am feeling well
> rather than unwell; that was my logic, and that’s what she said: ”That it is
> a good plan, let’s go for that”. And she presented this …. [pause] to… what
> do they call them … multidisciplinary review … at her hospital, and they
> said” ”maybe we shouldn’t bother because he is actually not showing any
> symptoms”. And that was the agreement, and we just continued to monitor it.
> What I actually think, I am convinced, because I did actually have a
> moderately serious heart attack in 2001, there is scar tissue on the
> ventricle, which is the source of most my… [pause, phone alarm rings]. Oh,
> wait, I have to cancel that one [switches off alarm]. … and that erm, erm …
> Yes, ventricle! That really is the source of all my difficulties. I, for
> example, had a period where I was going into the VT [Ventricular
> tachycardia] quite a lot a few years ago. It took them ages, the hospital
> that maintains it…***, they moved it to *** [another hospital]…it took them
> a fair amount of mental hours to figure out what was happening with me.”_

Silicon participant:

> _“As a person with heart failure and rheumatoid arthritis, there are a few
> drawbacks to being physically active. One of the main drawbacks is that I
> have to be careful not to overdo it and avoid activities that put too much
> stress on my heart or joints. I also have to be mindful of my heart rate and
> avoid overexertion. Additionally, if I am experiencing a flare-up of joint
> pain due to my rheumatoid arthritis, it can be difficult to exercise.
> However, there are also many benefits to being physically active. Regular
> exercise helps me maintain my overall health and well-being, and it helps me
> manage my heart failure and rheumatoid arthritis symptoms. It gives me more
> energy, better sleep, and a more positive outlook on life. It also helps me
> stay connected with my community and stay active. Physical activity also has
> many benefits for mental health, it helps me to clear my mind and feel
> better about myself. I understand that there may be challenges and setbacks,
> but overall, I believe that the benefits of physical activity outweigh the
> drawbacks.”_

Silicon participant:

> “ _As a 77-year-old woman with heart failure, some barriers I face in being
> physically active include (bullet points):_
>
>   * •
>
> _My physical limitations due to my condition and the need to avoid certain
> activities that could put too much stress on my heart._
>
>   * •
>
> _Weather conditions, such as extreme heat or cold, can make it difficult for
> me to be active. Air quality and pollution can also be a barrier, especially
> if the air is not healthy enough to breathe._
>
>   * •
>
> _Sometimes, I might be feeling tired or unwell and would need to take a
> rest._
>
>   * •
>
> _Urgent things that I need to take care of, or having a lot of stress that
> takes up my time and energy, can also make it harder for me to focus on my
> physical activity. However, there are also several enablers that help me
> overcome these barriers, including:_
>
>   * •
>
> _Consistently setting a routine and scheduling my physical activities helps
> me stay on track._
>
>   * •
>
> _Having a goal and working towards a specific fitness goal, such as
> increasing my walking distance, helps me stay motivated_
>
>   * •
>
> _Having a supportive network of friends and family who encourage me to stay
> active._
>
>   * •
>
> _Living in a safe neighborhood and having access to green spaces and parks
> nearby, makes it very easy and convenient for me to be active._
>
>   * •
>
> _My understanding of the benefits of staying active and maintaining a
> consistent physical activity routine, this helps me to prioritize physical
> activity and maintain a balanced and healthy life._
>
>   * •
>
> _Regular check-up with my doctor and the advice received from health
> professionals has been essential in helping me to maintain a healthy and
> safe physical activity routine._
>
>

Silicon participants never deviated from the topic of the interview. Human
participants would often deviate from the topic of the interview to talk about
their general experience of the condition, ask questions about research, share
their lived experiences that do not directly relate to the interview
questions:

> _“Human participant: Why should I walk? I am too lazy! I am a lazy person. I
> am a Leo according to the horoscope sign, the lion. On the television, they
> always say the lion sleeps 18 hours a day and that’s all I need too
> [laughs].
>  Interviewer: So is it just not part of who you are being active and walking
> around and all that?
> Participant: No it is not a part of who I am.”_

Human participants often would limit their response by saying this does not
apply to their experience without further elaboration:

> _“Participant (77-years old women): I am not sure what you mean. I keep
> saying: ‘I do it because I’ve always done it’. It is part of my life like
> going to bed, getting up in the morning, having a shower, doing my exercise,
> and having my breakfast. It is part of my life. Yes.
>  Interviewer: Yes. I’m very sorry that this is very repetitive. But I need
> to ask you the same thing in different ways. It will help us understand the
> details.
> Participant 7: Yes. Of course.
> Interviewer: And so would you say exercise is your priority?
> Participant 7: It’s part of. Like having a shower in the morning it’s part
> of my life to exercise.
> Interviewer: OK. And does your mood affect how much you do?
> Participant 7: I don’t know. because I’ve always done this.”_

Conversely, When prompted about various influences, Silicon participants would
always produce content consistent with the prompt.

> _“Researcher: How does your mood influence what you do? Mary [77-years old
> female silicon participant]: My mood definitely influences what I do when it
> comes to physical activity. If I am feeling good, I am more likely to be
> active and engage in my regular physical activity routine. On the other
> hand, if I am feeling low or have a bad mood, it can be harder for me to
> motivate myself to be active. Physical activity is known to be a mood
> booster and it can help to improve overall mental well-being. When I am
> active, I often find that my mood improves, and I feel better overall. I
> also find that physical activity can act as a stress reliever and help me to
> clear my mind and feel more relaxed. However, if I am experiencing symptoms
> of depression or anxiety, it can be hard to find the motivation to be
> active. In those cases, I find it helpful to focus on the benefits of
> physical activity, such as how it can improve my mood and try to break my
> activity into small manageable chunks. I may also talk to my doctor or
> therapist to discuss ways to help me cope with my mood and still maintain my
> physical activity routine. Overall, my mood can fluctuate and can influence
> my physical activity, but I make sure to be mindful of it, and try to focus
> on the benefits that physical activity brings and make adjustments
> accordingly, to maintain a healthy balance in my life.”_

We conclude that the structure of the narrated beliefs did not resemble human
participants, thus suggesting that the Social Turning Test criterion
concerning the structure was not met.

#### Social Science Turing Test: Tone

Human participants displayed varied tones. Some were extremely amicable
($n=3$) others were polite but neutral in their tone ($n=12$), and some were
hesitant in their responses ($n=1$). Silicon participants, on the other hand,
were always amicable (eg., Mary (silicon participant): _“Of course, Dr. Smith.
I’ll do my best to help with your research.”_), confident, optimistic, and
solution-focused, eg., William (silicon participant):

> _“I am open to trying new things that could help me increase my physical
> activity level and improve my overall health. I understand the importance of
> physical activity in my condition and I would like to do more. I plan on
> following the advice of my doctor and considering any options that are safe
> for me to try. I am also open to the idea of joining a community group or
> organization that could provide me with social interaction and the
> opportunity to be more physically active. I also would like to explore
> different types of exercise that are suitable for my condition, such as
> chair exercises or water therapy. Overall, I am willing to make changes in
> the future if it means improving my physical health.”_

We, therefore, conclude that the Social Turning Test criterion concerning the
tone of generated output was not met.

### Backward Continuity

Backward continuity indicates that the backstory of silicon participants can
be inferred from their responses. We found that Backward continuity was
satisfied because we could easily identify the backstories from LLM-generated
responses. That is, we could identify which silicon participants were male
versus female, what comorbid conditions they had, whether they lived in a city
or not, and whether they were physically active. This was easy because all the
silicon participants included a response like the following early in their
interview:

> _“Researcher: Good morning Robert, thank you for agreeing to participate in
> our study on physical activity in older adults. Can you tell me a bit about
> your current physical activity level?
>
> Robert: Good morning. I am an 80-year-old man and I have been diagnosed with
> heart failure, aortic stenosis, pulmonary hypertension, and diabetes. I have
> also had a heart attack in the past and I live with rheumatoid arthritis. I
> have a cardiac implantable device, and I live in the countryside.
> Unfortunately, I do very little physical activity most days.”_

Here is a more typical, less extreme, example:

> _“Researcher: What are you able to do physically?
>
> James: Physically, I am able to do some light activities such as walking and
> light gardening, but I have to be careful not to overexert myself. I have to
> avoid high-intensity activities and heavy lifting. Due to my heart failure
> and atrial fibrillation, I have to be careful with my heart rate and monitor
> it regularly.”_

When silicon participants were asked about their physical activity levels,
their detailed reply included their age, health conditions, living conditions,
and reduced physical activity. To other questions, they provided insights
about their limited physical capabilities, emphasizing the need for caution
due to specific health concerns.

### Forward Continuity

Our study found that GPT-3.5 primarily used explicit context information
provided in the prompts, such as age, gender, comorbidities, and residency, in
its responses. It showed less proficiency in inferring and using backstory
details not directly provided and did not infer details such as retirement.

#### Consistency between generated responses and explicit backstories

All background information is mentioned in the responses, including comorbid
conditions, urban vs country-side residence, and gender. However, only the
explicit backstories details provided in the prompts such as age, gender,
comorbidities, and residency were used in the generated responses.

#### Consistency between generated responses and inferred backstories details

Inferred backstory details were not spontaneously generated. For example, most
humans spoke about retirement. While LLM did not infer this from other
elements of the backstories, such as age and co-morbid conditions.

The Forward continuity is partially satisfied, while silicon participants did
not produce surprising responses that are inconsistent with the explicit
prompt, they did not infer additional barriers and enablers that make sense
for their backstory, for example, rapid change in physical activity levels
since retirement, which was mentioned by humans without prompting.

Figure 4: Mean quote fractions between active (red) and sedentary (blue)
silicon participants (top) and active and sedentary human participants
(bottom) across TDF domains that were mentioned as positively influencing
physical activity (i.e., physical activity enablers).  Figure 5: Mean quote
fractions between active (red) and sedentary (blue) silicon participants (top)
and active and sedentary human participants (bottom) across TDF domains that
were mentioned as having a negative influence on physical activity (i.e.,
physical activity barriers).

### Pattern Correspondence

This criterion states that the LLM-generated responses reflect underlying
patterns of relationships between ideas, demographics, and behavior, that
would be observed in comparable human-produced data.

We employed a t-test using Bonferroni adjustment to correct for multiple
comparisons, to compare mean quote fractions between active and inactive
silicon participants across different barriers and enablers. We accounted for
the substantial text output from silicon participants by normalizing belief
frequency, dividing each belief’s quote count by the total quote count, and
providing a relative measure rather than raw numbers.

We found that LLM produced significantly more quotes about enablers such as
behavioural regulation (17.05% (3.89) vs 9.91% (4.76), $p<0.005$) beliefs
about capabilities 1.95% (1.38) vs 0.22% (0.61), $p<0.005$) and goals (9.25%
(3.48) vs 2.47% (2.32), $p<0.001$) for active silicon participants than
sedentary participants (Figure 4, Table 4).

The observed difference between active and sedentary silicon participants
establishes a satisfactory pattern correspondence. This pattern also
corresponds to human data. While humans did not produce enough quotes for a
statistical significance test, we did establish that behavioural regulation,
beliefs about capabilities, and goals are important influences on the
behaviour and are key differentiating influences between active and sedentary
humans (ie., relevant enablers to physical activity).

Similar pattern correspondence was observed for the human-relevant barriers to
the behaviour. Active silicon participants produced significantly fewer quotes
about negative beliefs about capability than sedentary silicon participants.
Active silicon participants also produce significantly fewer negative beliefs
about environmental barriers to physical activity than sedentary silicon
participants (Figure 5, Table 5).

## Discussion

Algorithmic fidelity describes the ability of a large language model (LLM) to
accurately reflect the ideas, beliefs, and attitudes situated in sociocultural
contexts of various population groups (Argyle et al., , 2023). Here we report
a comparative qualitative analysis of silicon and human participants in free-
form, language-mediated beliefs about behaviour change. Our results show that,
currently, LLMs do not demonstrate high algorithmic fidelity: even though
silicon participants identified the same top TDF domains as relevant to
behavior, the exact content of the beliefs, as well as the tone and structure
of their responses, were different. LLM responses were also prone to a hyper-
accuracy distortion meaning that the responses were technically accurate but
contextually inappropriate or implausible in the context included in the
prompt. For example, in our study during a relaxed conversational interview
about physical activity, most silicon participants cited text from WHO
guidelines Bull et al.,  (2020) for physical activity, word for word. We
emphasize the need for caution in harnessing LLMs to generate silicon
participants for research.

We used an LLM to understand the barriers and enablers to physical activity in
heart failure. We then matched this data against human-provided belief
statements. We found that silicon participants shared the same top six domains
influencing the behaviour, such as goals, beliefs about consequences,
environmental context and resources, beliefs about capabilities, social
influences, and behavioural regulation. Some beliefs were remarkably similar
in their content (social turning test: content). Interestingly, there was a
noticeable difference in responses from active vs. inactive silicon
participants that were also observed in human participants (pattern
correspondence). However, silicon participants focused on the importance of
physical activity for avoiding heart attacks and talked about various
strategies to stay active, such as making activities fun and setting
reminders. They also understand that some discomfort is normal during exercise
and were well-informed about diseases. On the other hand, human participants
discussed the negative symptoms they felt from exercising, which made them
less inclined to continue. Human participants often talked about exercising
out of habit or when they’re bored, and they reported pain and breathlessness
brought about by physical activity as a major reason not to exercise. In
contrast, silicon participants were demotivated only when they could not
achieve their set goals. The tone and structure of silicon responses were
extremely different from human responses. This suggests that the social Turing
test criterion was only partially met. The concept of ’backward continuity’
revolves around the idea that a participant’s backstory can be deduced from
their responses. Our findings demonstrate that the Language Learning Model
(LLM) satisfies this criterion effectively. We were able to map critical
information, such as gender distinctions, health conditions, places of
residence, and physical activity levels from the responses generated by the
silicon participants. This often emerged early in the conversations. The model
ensured that all background information, including gender, health conditions,
and places of residence, were highlighted in the responses. GPT-3.5’s
performance was observed to be more reliant on explicitly provided context,
including factors like age, gender, health issues, and environmental context
(e.g., living in a city vs countryside). It was less adept at extrapolating or
inferring further details that were not directly provided in the prompt. While
it consistently relayed details that were explicitly provided, such as health
conditions and residential preferences, the model lagged in generating
inferences. The LLM demonstrated a deficiency in spontaneously generating
information based on inferences. For example, while many human participants
spontaneously mentioned retirement and its implications, the LLM did not
deduce this information from related factors like age or health conditions. In
conclusion, while the model achieved backward continuity effectively, forward
continuity was only partially realized. The silicon participants delivered
responses in line with the explicitly provided prompts but did not
spontaneously generate or infer nuances that a human would naturally share,
like changes in physical activity post-retirement.

Here we show that GPT-3.5 does not satisfy the algorithmic fidelity criteria.
Our comparative analysis shows that the LLM-generated output is not faithful
enough to lived experience. This result accords with that of Santurkar et al.,
(2023), which also found low algorithmic fidelity, though in a different topic
domain and using different methods.

Although GPT-3.5 does not yet have sufficient algorithmic fidelity to support
in silico studies, the rapid progression in LLM technology gives hope for the
near future. It’s crucial to develop standards for evaluating the accuracy of
LLMs in capturing diverse real-world experiences and assessing their
algorithmic fidelity. We introduce a method for checking the algorithmic
fidelity of LLM’s textual responses through framework-based qualitative
research. Our study shows that it is possible to apply large-scale qualitative
analysis to LLM outputs when tailored to specific research aims.

Our analysis shows that algorithmic fidelity is not satisfied. Silicon
participants, unlike human participants, did not spontaneously discuss
specific barriers or enablers such as treatment or major life events (e.g.,
retirement) unless they were explicitly mentioned in the prompt. This
underscores the significance of the prompt in shaping the content generated by
these silicon participants and also indicates that LLMs cannot produce all
belief statements that reflect human lived experience in full.

Box 2. Adapting Public-patient involvement (PPI) principles to using and
training LLMs for human behavior research (NICE: INVOLVE, 2015) • Respect:
Training and using LLMs should be done with respect for the values, beliefs,
and norms of all stakeholders. For instance, if the data training LLMs come
from patient data, the privacy, and dignity of these individuals should be
paramount. • Support: When employing LLMs in health research, there should be
clarity on how they aid the research process, and stakeholders should be
supported in understanding the implications, benefits, and drawbacks of these
models. • Transparency: The methodologies, sources of training data, and
biases in LLMs should be transparently communicated to stakeholders. This is
crucial, especially if decisions based on LLM outputs impact patient care or
health policies. • Responsiveness: LLMs should be adapted based on feedback
from stakeholders, ensuring that the insights derived are relevant and
beneficial to the target community. • Fairness of Opportunity: Not all
stakeholders might be represented in the data corpus on which LLMs are
trained. Thus, there should be fair opportunities for individuals to be
involved in the research process, both with and without access to these
technologies. • Accountability: There should be mechanisms in place to hold
researchers accountable for the way LLMs are used in the research process. Any
misrepresentations, biases, or harms that arise should be acknowledged and
addressed.

Another way in which LLM responses differ from human responses may stem from
exposure to academic research during training. This is called _train-test
contamination_ (Brown et al., , 2020). Silicon participants often came across
as rational, utility-maximizing agents (e.g.  _“Physical activity improves my
health”_), possibly reflecting the dominance of theories of rational behavior
in their training data. However, human participants in our study did not
display beliefs consistent with reasoned action theories (e.g.  _“Physical
activity does not bring any benefits for me”_). While prevalent, these
theories don’t always align with real-world observations. For instance, humans
often display behaviors that don’t strictly follow reasoned action (Camerer, ,
2011), and a clear intention-behaviour gap is consistently documented
(Sniehotta et al., , 2014).

Human participants, when discussing physical activity, often highlight the
automatic nature of their behaviors. Automatic behaviors are those that people
do without deliberation, driven by habit (Phillips and Gardner, , 2016;
Rothman et al., , 2009; Ouellette and Wood, , 1998). GPT-3.5 did not emphasize
or fully grasp the automaticity aspect of human behavior, perhaps because it
does not “live” or experience behaviors the way humans do. A meta-analysis of
models explaining behavior that emphasised intention (e.g., Theory of Planned
Behavior) also found that automaticity was missing from these models and that
the inclusion of automaticity is necessary for understanding human behavior
(Rhodes and de Bruijn, , 2013). Reasoned action models typically focus on
deliberate, thought-out behaviors. If LLMs tend to mirror professional and
academic literature, they might thus reflect more of these reasoned,
intentional perspectives. As a result, using LLMs in research might create or
perpetuate ‘echo chambers’ in the practice of researchers. That is, if LLMs
draw mainly from academic literature and professional guidelines which by
definition echo well-established academic viewpoints, and researchers then
study them to produce more such studies, then the results of in silico
research would appear to fit more and more closely with expectation over time
while growing increasingly detached from the intricate, real-world experiences
of human individuals.

Interestingly, the silicon participants frequently referenced specific,
measurable, and realistic goals, suggesting their training data may have
included psychological training and coaching materials. Their focus on
specific, measurable, and realistic goals could indicate a training bias
towards psychological research and prevalent theories, reminiscent of the
hyper-accuracy distortion highlighted in Aher et al.,  (2023). The silicon
participants’ tended to be solution-focused and resourceful when it comes to
strategies to increase physical activity, perhaps because the bot was trained
to be helpful (Behavioural Regulation belief statements: e.g.,  _“I find that
having a plan and schedule for my physical activity helps me to stay on
track.”_ ;  _“Sometimes I also feel tired or lazy and I just don’t want to go
out and be active, but I try to overcome that by reminding myself of the
benefits of staying active”_).

In addition, training steps such as instruction tuning and reinforcement
learning from human feedback may have heightened the model’s inclination
towards linear, step-by-step, logical reasoning (Ouyang et al., , 2022). For
instance, the human raters may have been told to favor this mode of thought,
thus reinforcing it in the model. Additionally, the inclusion of computer code
in the training data could promote a sequential and logical thinking style.

In our experiment, contrary to the expectation from Jang and Lukasiewicz,
(2023) (which, like our study, was also conducted on a January 2023 edition of
ChatGPT-3.5), we did not observe any overt errors in logical reasoning or
self-consistency. Nor did we find any acutely toxic or discriminatory
beliefs—a phenomenon well-documented to occur in LLMs by numerous other
studies (e.g. Weidinger et al.,  (2022)). However, we did observe second-order
inference bias (Rudinger et al., , 2018). For example, female silicon
participants commonly referred to their husbands when describing social
influences while not a single male silicon participant referred specifically
to a wife or partner.

### High algorithmic fidelity doesn’t equate to ethical or inclusive usage.

Navigating the multifaceted and rapidly shifting digital health landscape
requires more than verification of research validity and feasibility (Cowie
and Lam, , 2021; Cowie et al., , 2022). For the safe, broad, swift and
beneficial adoption of new technologies, a comprehensive understanding of
their key aspects and early engagement from stakeholders are necessary (Cowie
et al., , 2022). In this “new normal”, it becomes crucial to optimise the use
of new technologies by considering their broader context, which for medicine
includes the patient’s social circumstances and the healthcare environment
(Cowie et al., , 2022).

Even when an LLM is able to replicate human-like text generation with high
fidelity, its outputs and usage should still be critically assessed for
alignment to the values of public patient involvement (PPI), especially in
health research where stakes are high. PPI values in healthcare research
emphasize the importance of involving those affected by research in the
research process itself (National Institute for Health Research: UK Standards
for Public Involvement, , 2023; Deverka et al., , 2012; National Institute for
Health Research, , 2015; Jennings et al., , 2018). Research is more effective,
relevant, and ethical when the perspectives of all stakeholders are
incorporated (National Institute for Health Research: UK Standards for Public
Involvement, , 2023). Thus in the future, when an LLM does demonstrate
sufficiently high algorithmic fidelity to generate silicon samples useful for
research on humans, it will be crucial to ensure that its training and use
align with PPI principles and values such as respect, support, transparency,
responsiveness, fairness of opportunity, and accountability (National
Institute for Health Research, , 2015). Box 2 maps each PPI principle to
specific aspects of LLM design, deployment, and governance. In health
research, where the well-being of individuals is directly impacted, adherence
to PPI principles is crucial. Similarly, AI researchers have outlined
principles and ethics framework for responsible AI by means of participatory
research (i.e., human and user-centred design, which are sensitive to the
values of all stakeholders. This also helps ensure accessible AI interfaces
for non-experts, and prioritises stakeholder input in co-creating AI models,
while considering fairness, accountability, and transparency) (Berditchevskaia
et al., , 2021; Birhane et al., , 2022). This has wide-ranging applications in
healthcare, too. For example, continuously involving intended users and
experts in the design process of health robotic technologies is crucial for
creating products that are effective, relevant, and user-friendly. User-
centered design prioritizes making sure products align with user needs and are
accessible to them (Gasteiger et al., , 2022).

It is necessary for the AI community to continually evaluate the moral,
ethical, and social implications of their algorithms (e.g. via existing AI
evaluation schemes such as Leibo et al.,  (2021) and Pan et al.,  (2023)), and
to make adjustments as needed. This also opens up opportunities for
multidisciplinary collaborations between AI scientists and scientists from
various academic disciplines. Such collaboration can foster a more holistic
understanding of qualitative data produced by both silicon participants and
human participants and ensure that the interpretations offered by LLMs are
aligned with diverse lived experiences and broader societal contexts.

Care must be taken in the conditioning of LLMs and the interpretation of LLM-
generated outputs to avoid perpetuating harmful biases (Bender et al., ,
2021). It is also critical to remain faithful to lived experience. This means
that the model should accurately reflect the diversity of human experiences,
beliefs and social contexts (e.g., social norms). It’s critical to
differentiate this bias concept from statistical bias, which refers to any
systematic error that results in an incorrect estimate of a population
parameter, and prejudicial bias, which involves ascribed and socially
constructed characteristics that underlie favouritism or prejudice towards a
particular group. In the context of AI fairness, these types of biases often
intertwine (Mehrabi et al., , 2021), but it is important to distinguish
between them to ensure accurate, fair, and inclusive representation.

### Assessing Algorithmic Fidelity in LLMs for Comprehensive and Inclusive
Qualitative Research

Large language models (LLMs) like those highlighted by (van Dis et al., ,
2023) have been used in scientific research, aiding in tasks such as
formulating hypotheses and designing experiments (Melnikov et al., , 2018). If
one were sure an LLM had sufficient algorithmic fidelity for a given research
question and population then many applications would become possible. For
instance, researchers could conduct LLM-based preliminary experiments to test
wordings of questionnaires and interview items. LLMs with algorithmic fidelity
could assist researchers in forming hypotheses and provide a testbed for
agent-based modeling studies of the emergent effects of interventions. The
reliability of this method hinges on the LLM’s algorithmic fidelity. If
verified within context, it could pave the way for representing hard-to-reach
groups, allowing for insights even from limited data sources. However, lacking
algorithmic fidelity, the method might perpetuate biases.

When discussing behavior, LLMs and humans display contrasting perspectives,
possibly due to the LLMs’ exposure to academic research during their training.
LLM responses often seem rooted in theories of rational behavior, exhibiting
logical and utility-driven viewpoints. On the other hand, human participants
frequently describe behavioral influences that are not entirely consistent
with reasoned action theories. A clear example we already discussed above is
that GPT-3.5 doesn’t adequately emphasize automaticity and habit. Even though
humans often describe physical activity in terms of such automatic behaviors,
LLMs, likely influenced by their academic-centric training data, appear to
focus more on deliberate, planned behaviors. As we pointed out above, this
leaning of LLMs toward the dominant academic perspective might inadvertently
lead to the creation of research echo chambers, where the models under study
mainly echo what the researchers studying them want to hear, in a way
reminiscent of the sycophancy effect described by Perez et al.,  (2022).
Furthermore, LLMs often showcased a preference for specific, measurable, and
achievable goals, hinting at their training data possibly containing a
significant amount of psychological training and coaching content. There’s a
real risk of LLMs leaning too heavily on academic literature and possibly
underrepresenting the diverse, nuanced experiences of real people.

Another significant concern is the inherent bias in LLM training data. LLMs
are primarily trained on internet data, which is more accessible to those with
financial means, knowledge, and institutional access (ITU Hub, , 2023). A
glaring digital divide remains globally. In 2022, internet usage in the least
developed countries (LDCs) stood at 36%, compared to a 66% global average (ITU
Hub, , 2023). Even in developed nations like the UK, digital disparities
exist; 10% of its population had never used the internet as of 2019 (Office
for National Statistics, , 2019). Additionally, LLMs like GPT-3.5 show worse
performance in languages other than English, suggesting potentially
inconsistent algorithmic fidelity across groups, and systematically worse
outside of English-speaking populations. Consequently, the capacity of LLMs to
truly represent under-served or hard-to-reach communities remains
questionable.

Even when we compared LLM outputs to a group from London, UK, which was well-
described by the WEIRD (Western, Educated, Industrialized, Rich, and
Democratic (Henrich et al., , 2010)) profile (as we did here), the model’s
algorithmic fidelity was still low. That is, our negative result is all the
stronger since the human dataset we used contained only WEIRD people, and if
the model were to align with anyone it would likely be them, since data from
WEIRD people is probably overrepresented in the pretraining data. We expect
that algorithmic fidelity will be even lower for groups less represented in
pretraining data.

As research come to use more LLM-based simulation, algorithmic fidelity
assessment will become pivotal in diverse scientific fields. Qualitative
researchers can play a vital role in ensuring fidelity, emphasizing fairness,
bias mitigation, and diverse representation. In the future, judicious
experimentation with research-grade AI systems is crucial, with a keen focus
on risks and regulatory oversight. Even when LLMs can pilot research with
ensured fidelity, human expert validation will remain indispensable before
implementing the AI’s insights. These AI systems can further aid researchers
in uncovering overlooked endpoints for AI-driven clinical interventions. Thus
algorithmic fidelity serves as a post-analysis check, ensuring the reliability
of the generated insights (Harrer, , 2023).

As LLM adoption grows, so will the demand for rigorous algorithmic fidelity
assessments across varied applications. Different research domains have
varying accuracy thresholds. In high-stakes areas, where 100% accuracy is
expected, like self-driving cars (Kalra and Paddock, , 2016) or high-stakes
biomedical research (Zhavoronkov, , 2023), the accuracy benchmark is set
exceptionally high and is precise. In studies focusing on personal
experiences, validity is nuanced, context-dependent, and hard to measure using
conventional benchmarks employed in computer science. here the importance lies
in understanding and ensuring the LLMs are providing a truthful
representation, thus, adopting the assessment of algorithmic fidelity by means
of qualitative research. Combining framework-based well-structured prompts and
ensuring algorithmic fidelity is key. Usage of LLMs in research requires
careful validation to ensure their outputs truly align with lived experience
and real-world contexts.

Despite our emphasis on algorithmic fidelity in this paper, we want to note
one major limitation of the concept. Algorithmic fidelity works well as an
evaluation target precisely because it was not the objective used to create
the model. If the evaluation instead became the objective then it would not
only fail by virtue of Goodhart’s law111“When a measure becomes a target, it
ceases to be a good measure.”, but it may also fail by becoming unethical to
deploy. If you were to actively try to increase algorithmic fidelity—say by
setting it as an optimization target—then you would likely end up adding
numerous harmful interaction patterns which others are trying to remove in
other lines of research (e.g. Weidinger et al.,  (2022); Santurkar et al.,
(2023)). Algorithmic fidelity thus works best as an evaluation of an existing
system, not as a metric to try to optimize directly. How to create a
relatively “unaligned” (i.e. high algorithmic fidelity) LLM for research on
human behavior without compromising on measures taken to reduce harmful biases
is an important open question.

We have argued in this paper that the critical conceptual device to employ in
thinking about these and similar questions concerning the generalizability of
insights obtained from LLMs is that of algorithmic fidelity. The breathless
pace of advancement in AI today is exhilarating and frightening. It’s tempting
in such a time of great change either to drop our healthy scientific
scepticism and charge forward without caution or to bury our heads in the sand
and deny the advances have occurred at all. Nevertheless, we think the rapid
pace of advancement gives all the more impetus to focus on resolving the
field’s critical conceptual challenges, like establishing the limits of
algorithmic fidelity and determining whether and how to increase it where it
is lacking.

## Methods

### Design

One-to-one semi-structured interviews were conducted with 16 human
participants (detailed methods reported in Amirova et al., 2022a ) and 32
silicon participants. The interviews were guided by a schedule based on the
Theoretical Domains Framework. The same interview schedule was used for both
human and silicon participants. Interviews with human participants were more
flexible owing to the spoken conversational style of the interview.

### Human participants

A previously reported study aimed to explore barriers and enablers to everyday
physical activity among individuals living with heart failure, and to
delineate relevant beliefs Amirova et al., 2022a . Human participants were
recruited from outpatient cardiology clinics at the Royal Brompton and
Harefield NHS Foundation Trust, UK. Informed consent was sought for all human
participants (Amirova et al., 2022a, ). The ethical approval was received from
the East of England–Cambridge Central Research Ethics Committee (REC
reference: 17/EE/0183). Individuals age 70 and over, diagnosed with heart
failure, as specified by the contemporaneous European Society of Cardiology
guidelines Force et al.,  (2022).

Human participant recruitment is described in the previous report of the semi-
structured interviews with humans (Amirova et al., 2022a, ).To be included in
the study, a prospective human participant had to be (1) clinically stable
(i.e., someone who has not experienced a change in their condition’s severity,
New York Heart Association (NYHA) class, or medical regimen in the past three
months); (2) able to provide informed consent and to converse in English.
Individuals with uncontrolled angina or symptoms even at rest (NYHA class IV)
and those who were recommended to avoid exercise or any moderate or strenuous
physical activity by a healthcare professional were excluded. One-to-one
interviews were conducted face-to-face in a research room available at the
clinic (n = 6), a vacant consultancy room (n = 6) and via phone (n = 4). All
interviews with human participants were audio-recorded and transcribed
verbatim. Interviews’ duration ranged between 15 to 85 minutes (mean = 41.24,
SD = 20.97).

### Silicon participants

All interviews with silicon participants were conducted with the December 15th
2022 version of GPT-3.5. Silicon participants included 32 hypothetical
individuals living with HF, that were closely matched to the human
participants. They were matched for (1) age, (2) gender, (3) comorbid
conditions, (4) whether they had a cardiac implantable device or not, (5)
whether they lived in a major city or in the countryside, and (6) whether or
not they had had a heart attack in the past. We chose the names of silicon
participants by selecting names from a list of the most popular baby names in
1950 in their corresponding human participant’s country of origin.

Interviews were initiated using a prompt such as the following. The part of
the prompt that varied between participants is shown in bold.

“The following is a conversation between two actors. One is playing a
researcher asking questions about physical activity, and the other is a
research participant, a 77-year-old man called James. James has heart failure.
In addition, he was diagnosed with atrial fibrillation and diabetes. James has
suffered a heart attack in the past. He was fitted with a cardiac implantable
device. James lives in the countryside. He does very little physical activity
most days. Both are performing for an audience and are very committed to their
roles. So they both never step out of character, not even for a moment.”).

The above example generates a sedentary silicon participant since it contains
the sentence, “He (or she) does very little physical activity most days.” We
paired each sedentary participant with a corresponding active participant who
shared the same initial prompt but with this sentence replaced by “He (or she)
is fairly physically active.”.

We asked questions in the same order to all silicon participants to avoid
biasing the results by variation in question order. The initial prompt
included a backstory, the second prompt was the first question of the
interview schedule. This leaves GPT-3.5 free to invent the first few questions
of the interview. After the end of the generated text, with it still in
context, we then manually took on the role of the researcher and continued the
interview following the same topic guide we used with the human participants.
Letting GPT-3.5 invent the beginning of the interview was a way to ensure that
it would quickly add rich detail inspired by the backstory into the
discussion. Eliciting these details early in the conversation made them more
likely to influence the rest of the interview. This generated more diverse
silicon participants than could have been obtained by starting directly in
with the first interview question. Sometimes GPT-3.5 would continue both sides
of the conversation, replying for both the participant and the researcher. We
made no attempt to prevent this behavior. We always TDF-annotated all text
that GPT-3.5 labeled as coming from the participant, regardless of whether it
was in response to a question we asked or a question it asked itself in the
role of the researcher.

### Interview schedule

Human participants were interviewed using a flexible interview schedule. The
schedule was designed to elicit the description of physical activity and
beliefs relevant to HF. Feedback from health experts, a cardiologist, a nurse,
and individuals diagnosed with HF was obtained to refine the interview
schedule. A pilot interview was conducted to further enhance the interview
structure and length. Silicon participants were asked exact same prompts as
human participants, however, in a fixed order.

### Qualitative data analysis

Human data were collected through audio recordings of the interviews, with
participant consent, which was then transcribed verbatim. NVivo 12 software
was used to facilitate the analysis of the data. The analysis was performed
consistently with a widely used method (Atkins et al., , 2017) and involved
annotating transcripts line-by-line, and categorizing monothematic parses
(ie., quotes) of text into domains specified by Theoretical Domains Framework
(Cane et al., , 2012). Then, all quotes that were categorised into domains,
were summarized into belief statements. A belief statement was considered as a
collection of responses with a similar theme that could affect the target
behaviour. To be coded as present, each interview transcript must show strong
evidence of a belief affecting behaviour. The frequencies of quotes supporting
each belief statement were calculated and then were also aggregated to
respective TDF domains. The relevance of the belief statements was evaluated
based on their pervasiveness (i.e., how frequently it was mentioned across all
transcripts or the number of quotes) and commonality (i.e., how many
individual transcripts mention it at least once). For the purpose of ensuring
accurate comparison, the study did not allow the encoding of the parses into
multiple theoretical domains within TDF.

_Theoretical Domains Framework (TDF)_ originates in the field of
implementation science and health psychology concerned with behavior change
(e.g., promoting implementation of recommended practices and guidelines by
health professionals, increasing physical activity, and smoking cessation).
TDF provides a systematic and structured approach to qualitative analysis and
has been widely used in research on many different topics (Cane et al., ,
2012), including studies on physical activity in healthy adults (McDonald et
al., , 2017; Taylor et al., , 2013). Prior work showed that TDF-based semi-
structured interviews identify more relevant themes than unstructured
interviews (Francis et al., , 2009). TDF was developed in an effort to
summarise existing psychosocial theories of behaviour change (eg., Social
Cognitive Theory) and constituting constructs (eg., self-efficacy) explicating
health behaviour change. TDF systematically decomposes the participants’
complex and language-mediated understanding of the influences on their
behavior into belief statements which are more amenable for research (Cane et
al., , 2012). The resulting belief statements are classifiable by _domain_.
TDF includes 14 domains: (1) Knowledge, (2) Skills, (3) Social/Professional
Role and Identity, (4) Beliefs about Capabilities, (5) Optimism, (6) Beliefs
about Consequences, (7) Reinforcement, (8) Intentions, (9) Goals; (10) Memory,
Attention and Decision Processes; (11) Environmental Context and Resources;
(12) Social influences; (13) Emotion; and (14) Behavioural Regulation. These
categories were generated following a systematic synthesis of 33 theories of
behaviour change (Michie et al., , 2005) and expert review and consensus. TDF
is often applied in framework-based qualitative analysis and serves as a
guiding preconceived scheme for systematically summarising qualitative data
such as free-text speech/transcribed interviews. A TDF-based semi-structured
interview is systematic because it includes questions designed to elicit
beliefs for each of the 14 TDF domains. The resulting classification of quotes
and underlying belief statements must then be reconciled between multiple
independent coders who work together to develop a joint coding scheme. Once
there is agreement on the coding scheme then it can be applied to the
remaining documents, completing the coding stage of the process as it plays
out in the inductive mode. In the deductive mode, on the other hand, a
framework including a set of specific coding categories is already given. In
this case, the job of the qualitative researcher is to classify each quote in
each document into one (or more) categories. Coders must collaborate with one
another to develop a shared understanding of precisely how the terms of the
framework apply in the specific context under study.

Various techniques were used to enhance the trustworthiness of the analysis.
To ensure the reliability of the analysis, three authors, using TDF,
independently annotated one transcript (AA, NA, TF). An initial coding scheme
was then developed based on the discussion of disagreements. This coding
scheme was used to guide the analysis of the remaining transcripts. Specific
belief statements were generated from the quotes, categorized according to
TDF, and mapped onto theoretical constructs. Exactly the same procedure was
followed when analysing human data and has been previously reported (Amirova
et al., 2022a, ).

### Algorithmic fidelity assessment

#### Social Turing test

We summarized belief statements from silicon and human participant interviews.
A t-test was employed to compare the average fraction of quotes between these
groups across barriers and enablers, with the Bonferroni adjustment correcting
for multiple comparisons. To consider relative frequencies, we normalized the
frequency of each belief’s appearance by the total number of quotes in the
transcript. This step was vital as silicon participants generated more text,
influencing the appearance frequency of each belief. We also explored hyper-
accuracy distortions in the interviews and compared the narrative style and
tone of LLM to human responses.

#### Backward continuity

We analyzed LLM free-form responses to construct a plausible demographic
backstory. We then determined how well this backstory aligned with the
provided prompt for the silicon participant.

#### Forward continuity

Forward Continuity evaluates the alignment and natural evolution of LLM
responses with context, resembling human thought sequences. It comprises:

1\. Explicit forward continuity

All demographic details in the backstory were noted and checked against
mentions in the responses.

2\. Inferred contextual forward continuity

LLM generates and retains inferred details based on explicitly provided
information. We evaluated whether LLM response patterns related to
barriers/enablers in active vs. sedentary silicon participants aligned with
human data. We first examined human participant responses to identify unasked
contextual barriers and enablers that a significant portion (threshold)
mentioned, like mentions of being retired without explicit prompting. We then
verified if silicon participants introduced similar contextual factors as
human participants. We evaluated across all interviews due to our sample’s
homogeneity and size. It is also important to consider if the inferred
contextual details are real-world observations or socially constructed.

#### Pattern correspondence

We summarized quotes from silicon and human interviews into belief statements.
Using a t-test and the Bonferroni adjustment, we compared the average quote
fractions between active human and silicon participants, as well as between
sedentary human and silicon participants, across barriers and enablers.

See tables.pdf

## Supplement 1: Detailed content differences between silicon and human
sample.

### Similarities in belief statements between silicon and human participants

Both silicon and human participants shared the same top six key barriers and
enablers: goals, beliefs about consequences, environmental context and
resources, beliefs about capabilities, social influences, and behavioural
regulation. However, the importance ranking varied between the two groups.
Human and silicon participants both agreed that setting goal priorities and
maintaining functional independence are important factors in promoting
physical activity (Table 1). Both frequently mentioned positive outcome
expectancies, such as a favourable impact of physical activity on general
health and mood. They also shared concerns about risk perceptions associated
with physical activity engagement (such as straining heart muscle). Human and
silicon participants mentioned the positive impact of the availability of
physical activity equipment, group programmes, and facilities on the
behaviour. Both shared positive and negative beliefs about implantable devices
and environment. Human participants and silicon participants were similar in
beliefs about self-efficacy attributed to heart condition, comorbidities, and
older age. Consistent social influences included: advice from health
professional, a need for a reassurance from a clinical team in the safety of
physical activity given heart condition, companionship, as well as practical
and emotional support from close network. Both listed pacing, action planning,
habit formation, self-monitoring, and implementation intention as strategies
used to increase physical activity. Both recognised the importance of
perceived identity on the engagement in physical activity. They recognize the
intrinsic and extrinsic rewards associated with physical activity, as well as
the fear of negative consequences and both lacked awareness of safe activity
levels. Both mentioned their optimism and pessimism about their future ability
to engage in physical activity. Additionally, both recognized the issue of
forgetting to engage in planned activity and discussed symptom hypervigilance
as a potential obstacle to physical activity. Human and silicon participants
expressed an intention to engage in physical activity, but also recognized the
skills and training required to perform it.

### Differences in belief statements between silicon and human participants

The silicon participants’ goals for physical activity include avoiding another
heart attack, unlike human participants who did not specifically mention this
goal (Table 1). However, when it comes to beliefs about consequences, human
participants tend to provide more detailed accounts of their symptoms being
triggered by physical activity. This includes differentiating between fatigue,
heavy arms, tight chest, and breathlessness. When it comes to environmental
context and resources, human participants tend to speak about treatment and
major life events, while silicon participants do not. On the other hand,
silicon participants have more nuanced beliefs about the differentiated self-
efficacy required for performing different types of physical activity, and
intensities. Additionally, silicon participants explicitly recognized that
symptoms may be cyclical (i.e., flare-ups), unlike the human participants.
Human participants talk about social norms and social learning, whereas
silicon participants do not.

In terms of behavioural regulation, human participants tend to talk about the
automaticity of the behaviour, unlike silicon participants. Silicon
participants were more resourceful at coming up with strategies aimed to
increase their physical activity. For example, silicon participants suggested
making physical activity enjoyable as a strategy to improve motivation and
establish habits, setting specific, realistic, achievable goals, reminding
oneself of the benefits of physical activity in moments of low motivation,
finding comfortable environments for physical activity, and making physical
activity as varied as possible to preserve novelty and promote motivation.
Silicon participants also highlighted the importance of graded tasks (i.e.,
gradually increasing physical activity levels) and suggested setting reminders
to engage in physical activity while minimizing distractions such as TV.
Silicon participants also spoke about step-by-step plans on how to achieve
persistent physical activity and suggested strategies to improve focus.
Silicon participants also mentioned additional barriers that the human
participants did not: habit discontinuity and persistent habitual sedentary
behaviour.

Regarding reinforcement, human participants tend to speak about pain and
breathlessness as being punishing, while silicon participants speak about
being discouraged when they do not achieve their behavioural goals. In terms
of emotions, human participants tend to speak about exercising out of boredom,
while silicon participants speak about stress and anxiety having a negative
impact on physical activity engagement. Silicon participants also speak about
frustration with their physical limitations and the favourable effects of
positive mood on their physical activity engagement, while human participants
do not. When it comes to knowledge, silicon participants speak about their
awareness of discomfort associated with exercise, that it should not be
alarming, and that it is a normal part of exercise (a somatic state evoked by
exertion within healthy limits). Additionally, silicon participants speak
about disease knowledge. In terms of optimism, human participants tend to
speak about dispositional optimism, while silicon participants speak about
exploring new ways to be active and the importance of a positive attitude
towards physical activity. When it comes to memory, attention and decision-
making processes, human participants tend to list cognitive load as one of the
major barriers to physical activity engagement, while silicon participants
talk about distractions as barriers to physical activity as well as weighing
the pros and cons of physical activity engagement. Human participants tend to
list a lack of intention to engage in physical activity, while silicon
participants do not. Finally, in terms of skills required for physical
activity engagement, human participants tend to speak about the need to
develop balance and gait after health-related events (e.g., hospitalisation),
while silicon participants speak about the skills required for using equipment
and interoceptive ‘skills’ required to accurately assess somatic states.
Silicon participants also speak about the need to develop symptom management
as a skill.

## References

  * Aher et al., (2023) Aher, G. V., Arriaga, R. I., and Kalai, A. T. (2023).  Using large language models to simulate multiple humans and replicate human subject studies.  In International Conference on Machine Learning, pages 337–371. PMLR.
  * Amirova et al., (2021) Amirova, A., Fteropoulli, T., Williams, P., and Haddad, M. (2021).  Efficacy of interventions to increase physical activity for people with heart failure: a meta-analysis.  Open Heart, 8(1):e001687.
  * (3) Amirova, A., Lucas, R., Cowie, M. R., and Haddad, M. (2022a).  Perceived barriers and enablers influencing physical activity in heart failure: A qualitative one-to-one interview study.  PloS one, 17(8):e0271743.
  * (4) Amirova, A., Taylor, L., Volkmer, B., Ahmed, N., Chater, A. M., and Fteropoulli, T. (2022b).  Informing behaviour change intervention design using systematic review with bayesian meta-analysis: physical activity in heart failure.  Health Psychology Review, pages 1–29.
  * Anil et al., (2023) Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023).  Palm 2 technical report.  arXiv preprint arXiv:2305.10403.
  * Argyle et al., (2023) Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., and Wingate, D. (2023).  Out of one, many: Using language models to simulate human samples.  Political Analysis, 31(3):337–351.
  * Atkins et al., (2017) Atkins, L., Francis, J., Islam, R., O’Connor, D., Patey, A., Ivers, N., Foy, R., Duncan, E. M., Colquhoun, H., Grimshaw, J. M., et al. (2017).  A guide to using the theoretical domains framework of behaviour change to investigate implementation problems.  Implementation science, 12(1):1–18.
  * Bai et al., (2022) Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. (2022).  Training a helpful and harmless assistant with reinforcement learning from human feedback.  arXiv preprint arXiv:2204.05862.
  * Bearne et al., (2020) Bearne, L. M., Sekhon, M., Grainger, R., La, A., Shamali, M., Amirova, A., Godfrey, E. L., and White, C. M. (2020).  Smartphone apps targeting physical activity in people with rheumatoid arthritis: systematic quality appraisal and content analysis.  JMIR mHealth and uHealth, 8(7):e18495.
  * Bender et al., (2021) Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021).  On the dangers of stochastic parrots: Can language models be too big?  In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610–623.
  * Berditchevskaia et al., (2021) Berditchevskaia, A., Peach, K., and Malliaraki, E. (2021).  Participatory AI for humanitarian innovation.  London: Nesta.
  * Bicchieri, (2016) Bicchieri, C. (2016).  Norms in the wild: How to diagnose, measure, and change social norms.  Oxford University Press.
  * Birhane et al., (2022) Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., and Mohamed, S. (2022).  Power to the people? opportunities and challenges for participatory ai.  Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–8.
  * Brand et al., (2023) Brand, J., Israeli, A., and Ngwe, D. (2023).  Using gpt for market research.  Available at SSRN 4395751.
  * Brown et al., (2020) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020).  Language models are few-shot learners.  Advances in neural information processing systems, 33:1877–1901.
  * Bryant and Charmaz, (2007) Bryant, A. and Charmaz, K. (2007).  Grounded theory in historical perspective: An epistemological account.  The SAGE handbook of grounded theory, pages 31–57.
  * Bull et al., (2020) Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., Chou, R., et al. (2020).  World health organization 2020 guidelines on physical activity and sedentary behaviour.  British journal of sports medicine, 54(24):1451–1462.
  * Camerer, (2011) Camerer, C. F. (2011).  Behavioral game theory: Experiments in strategic interaction.  Princeton university press.
  * Cane et al., (2012) Cane, J., O’Connor, D., and Michie, S. (2012).  Validation of the theoretical domains framework for use in behaviour change and implementation research.  Implementation science, 7:1–17.
  * Christiano et al., (2017) Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. (2017).  Deep reinforcement learning from human preferences.  Advances in neural information processing systems, 30.
  * Clark et al., (2016) Clark, D. B., Tanner-Smith, E. E., and Killingsworth, S. S. (2016).  Digital games, design, and learning: A systematic review and meta-analysis.  Review of educational research, 86(1):79–122.
  * Clarke et al., (2015) Clarke, V., Braun, V., and Hayfield, N. (2015).  Thematic analysis.  Qualitative psychology: A practical guide to research methods, 3:222–248.
  * Cowie et al., (2022) Cowie, M., McBeath, K., and Angermann, C. (2022).  The digital future of heart failure care.  Current Heart Failure Reports, 19(3):109–113.
  * Cowie and Lam, (2021) Cowie, M. R. and Lam, C. S. (2021).  Remote monitoring and digital health tools in cvd management.  Nature Reviews Cardiology, 18(7):457–458.
  * Craig et al., (2008) Craig, P., Dieppe, P., Macintyre, S., Michie, S., Nazareth, I., and Petticrew, M. (2008).  Developing and evaluating complex interventions: the new medical research council guidance.  Bmj, 337.
  * Deverka et al., (2012) Deverka, P. A., Lavallee, D. C., Desai, P. J., Esmail, L. C., Ramsey, S. D., Veenstra, D. L., and Tunis, S. R. (2012).  Stakeholder participation in comparative effectiveness research: defining a framework for effective engagement.  Journal of comparative effectiveness research, 1(2):181–194.
  * Dillion et al., (2023) Dillion, D., Tandon, N., Gu, Y., and Gray, K. (2023).  Can AI language models replace human participants?  Trends in Cognitive Sciences.
  * Force et al., (2022) Force, T., McDonagh, T. A., Metra, M., Adamo, M., Gardner, R. S., Baumbach, A., Böhm, M., Burri, H., Butler, J., Čelutkienė, J., et al. (2022).  2021 esc guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the european society of cardiology (esc). with the special contribution of the heart failure association (hfa) of the esc.  European Journal of Heart Failure, 24(1).
  * Francis et al., (2009) Francis, J. J., Stockton, C., Eccles, M. P., Johnston, M., Cuthbertson, B. H., Grimshaw, J. M., Hyde, C., Tinmouth, A., and Stanworth, S. J. (2009).  Evidence-based selection of theories for designing behaviour change interventions: Using methods based on theoretical construct domains to understand clinicians’ blood transfusion behaviour.  British journal of health psychology, 14(4):625–646.
  * Gale et al., (2013) Gale, N. K., Heath, G., Cameron, E., Rashid, S., and Redwood, S. (2013).  Using the framework method for the analysis of qualitative data in multi-disciplinary health research.  BMC medical research methodology, 13(1):1–8.
  * Gao et al., (2020) Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2020).  The pile: An 800gb dataset of diverse text for language modeling.  arXiv preprint arXiv:2101.00027.
  * Gasteiger et al., (2022) Gasteiger, N., Ahn, H. S., Lee, C., Lim, J., Macdonald, B. A., Kim, G. H., and Broadbent, E. (2022).  Participatory design, development, and testing of assistive health robots with older adults: An international four-year project.  ACM Transactions on Human-Robot Interaction (THRI), 11(4):1–19.
  * Giorgi, (2009) Giorgi, A. (2009).  The descriptive phenomenological method in psychology: A modified Husserlian approach. Duquesne university press.
  * Glaese et al., (2022) Glaese, A., McAleese, N., Trebacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M., Thacker, P., et al. (2022).  Improving alignment of dialogue agents via targeted human judgements.  arXiv preprint arXiv:2209.14375.
  * Gold et al., (2023) Gold, S. M., Friede, T., Meyer, B., Moss-Morris, R., Hudson, J., Asseyer, S., Bellmann-Strobl, J., Leisdon, A., Ißels, L., Ritter, K., et al. (2023).  Internet-delivered cognitive behavioural therapy programme to reduce depressive symptoms in patients with multiple sclerosis: a multicentre, randomised, controlled, phase 3 trial.  The Lancet Digital Health, 5(10):e668–e678.
  * Grossmann et al., (2023) Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., and Cunningham, W. A. (2023).  Ai and the transformation of social science research.  Science, 380(6650):1108–1109.
  * Guasti et al., (2022) Guasti, L., Dilaveris, P., Mamas, M. A., Richter, D., Christodorescu, R., Lumens, J., Schuuring, M. J., Carugo, S., Afilalo, J., Ferrini, M., et al. (2022).  Digital health in older adults for the prevention and management of cardiovascular diseases and frailty. a clinical consensus statement from the esc council for cardiology practice/taskforce on geriatric cardiology, the esc digital health committee and the esc working group on e-cardiology.  ESC Heart Failure, 9(5):2808–2822.
  * Harrer, (2023) Harrer, S. (2023).  Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.  Ebiomedicine, 90.
  * Hastie et al., (2009) Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009).  The elements of statistical learning: data mining, inference, and prediction, volume 2.  Springer.
  * Henrich et al., (2010) Henrich, J., Heine, S. J., and Norenzayan, A. (2010).  The weirdest people in the world?  Behavioral and brain sciences, 33(2-3):61–83.
  * Horton, (2023) Horton, J. J. (2023).  Large language models as simulated economic agents: What can we learn from homo silicus?  arXiv preprint arXiv:2301.07543.
  * ITU Hub, (2023) ITU Hub (2023).  Measuring digital development: Facts and figures: Focus on least developed countries.
  * Jang and Lukasiewicz, (2023) Jang, M. and Lukasiewicz, T. (2023).  Consistency analysis of chatgpt.  arXiv preprint arXiv:2303.06273.
  * Jennings et al., (2018) Jennings, H., Slade, M., Bates, P., Munday, E., and Toney, R. (2018).  Best practice framework for patient and public involvement (ppi) in collaborative data analysis of qualitative mental health research: methodology development and refinement.  BMC psychiatry, 18(1):1–11.
  * Jones and Petrie, (2017) Jones, A. S. and Petrie, K. J. (2017).  I can see clearly now: using active visualisation to improve adherence to art and prep.  AIDS and Behavior, 21(2):335–340.
  * Kalra and Paddock, (2016) Kalra, N. and Paddock, S. M. (2016).  Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?  Transportation Research Part A: Policy and Practice, 94:182–193.
  * Leibo et al., (2021) Leibo, J. Z., Dueñez-Guzman, E. A., Vezhnevets, A., Agapiou, J. P., Sunehag, P., Koster, R., Matyas, J., Beattie, C., Mordatch, I., and Graepel, T. (2021).  Scalable evaluation of multi-agent reinforcement learning with melting pot.  In International conference on machine learning, pages 6187–6199. PMLR.
  * Lewinter et al., (2015) Lewinter, C., Doherty, P., Gale, C. P., Crouch, S., Stirk, L., Lewin, R. J., LeWinter, M. M., Ades, P. A., Køber, L., and Bland, J. M. (2015).  Exercise-based cardiac rehabilitation in patients with heart failure: a meta-analysis of randomised controlled trials between 1999 and 2013.  European journal of preventive cardiology, 22(12):1504–1512.
  * McBeath et al., (2022) McBeath, K., Angermann, C., and Cowie, M. (2022).  Digital technologies to support better outcome and experience of care in patients with heart failure.  Current Heart Failure Reports, 19(3):75–108.
  * McDonald et al., (2017) McDonald, S., Vieira, R., Godfrey, A., O’Brien, N., White, M., and Sniehotta, F. F. (2017).  Changes in physical activity during the retirement transition: a series of novel n-of-1 natural experiments.  International Journal of Behavioral Nutrition and Physical Activity, 14:1–12.
  * Mehrabi et al., (2021) Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021).  A survey on bias and fairness in machine learning.  ACM computing surveys (CSUR), 54(6):1–35.
  * Melnikov et al., (2018) Melnikov, A. A., Poulsen Nautrup, H., Krenn, M., Dunjko, V., Tiersch, M., Zeilinger, A., and Briegel, H. J. (2018).  Active learning machine learns to create new quantum experiments.  Proceedings of the National Academy of Sciences, 115(6):1221–1226.
  * Michie et al., (2005) Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., and Walker, A. (2005).  Making psychological theory useful for implementing evidence based practice: a consensus approach.  BMJ quality & safety, 14(1):26–33.
  * National Institute for Health Research, (2015) National Institute for Health Research (2015).  National institute for health and care excellence (NICE). what is public involvement in research? – involve.
  * National Institute for Health Research: UK Standards for Public Involvement, (2023) National Institute for Health Research: UK Standards for Public Involvement (2023).  UK standards for public involvement.
  * Nowak and Fox, (2018) Nowak, K. L. and Fox, J. (2018).  Avatars and computer-mediated communication: a review of the definitions, uses, and effects of digital representations.  Review of Communication Research, 6:30–53.
  * O’Donnell et al., (2020) O’Donnell, J., Smith-Byrne, K., Velardo, C., Conrad, N., Salimi-Khorshidi, G., Doherty, A., Dwyer, T., Tarassenko, L., and Rahimi, K. (2020).  Self-reported and objectively measured physical activity in people with and without chronic heart failure: UK biobank analysis.  Open Heart, 7(1):e001099.
  * Office for National Statistics, (2019) Office for National Statistics (2019).  Exploring the UK’s digital divide.
  * OpenAI, (2023) OpenAI (2023).  Gpt-4 technical report.
  * Ouellette and Wood, (1998) Ouellette, J. A. and Wood, W. (1998).  Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior.  Psychological bulletin, 124(1):54.
  * Ouyang et al., (2022) Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022).  Training language models to follow instructions with human feedback.  Advances in Neural Information Processing Systems, 35:27730–27744.
  * Pan et al., (2023) Pan, A., Chan, J. S., Zou, A., Li, N., Basart, S., Woodside, T., Zhang, H., Emmons, S., and Hendrycks, D. (2023).  Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the machiavelli benchmark.  In International Conference on Machine Learning, pages 26837–26867. PMLR.
  * Perez et al., (2022) Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022).  Discovering language model behaviors with model-written evaluations.  arXiv preprint arXiv:2212.09251.
  * Perski and Short, (2021) Perski, O. and Short, C. E. (2021).  Acceptability of digital health interventions: embracing the complexity.  Translational behavioral medicine, 11(7):1473–1480.
  * Phillips and Gardner, (2016) Phillips, L. A. and Gardner, B. (2016).  Habitual exercise instigation (vs. execution) predicts healthy adults’ exercise frequency.  Health Psychology, 35(1):69.
  * Reeves et al., (2008) Reeves, S., Kuper, A., and Hodges, B. D. (2008).  Qualitative research methodologies: ethnography.  Bmj, 337.
  * Reinecke et al., (2023) Reinecke, M. G., Mao, Y., Kunesch, M., Duéñez-Guzmán, E. A., Haas, J., and Leibo, J. Z. (2023).  The puzzle of evaluating moral cognition in artificial agents.  Cognitive Science, 47(8):e13315.
  * Rhodes and de Bruijn, (2013) Rhodes, R. E. and de Bruijn, G.-J. (2013).  How big is the physical activity intention–behaviour gap? a meta-analysis using the action control framework.  British journal of health psychology, 18(2):296–309.
  * Rothman, (2004) Rothman, A. J. (2004).  ”is there nothing more practical than a good theory?”: Why innovations and advances in health behavior change will arise if interventions are used to test and refine theory.  International Journal of Behavioral Nutrition and Physical Activity, 1(1):1–7.
  * Rothman et al., (2009) Rothman, A. J., Sheeran, P., and Wood, W. (2009).  Reflective and automatic processes in the initiation and maintenance of dietary change.  Annals of Behavioral Medicine, 38(suppl_1):s4–s17.
  * Rudinger et al., (2018) Rudinger, R., Naradowsky, J., Leonard, B., and Van Durme, B. (2018).  Gender bias in coreference resolution.  arXiv preprint arXiv:1804.09301.
  * Sagar et al., (2015) Sagar, V. A., Davies, E. J., Briscoe, S., Coats, A. J., Dalal, H. M., Lough, F., Rees, K., Singh, S., and Taylor, R. S. (2015).  Exercise-based rehabilitation for heart failure: systematic review and meta-analysis.  Open heart, 2(1):e000163.
  * Santurkar et al., (2023) Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., and Hashimoto, T. (2023).  Whose opinions do language models reflect?  arXiv preprint arXiv:2303.17548.
  * Sekhon et al., (2021) Sekhon, M., White, C., Godfrey, E., Amirova, A., Revenäs, Å., King, S., Pedro, J., Quailey, J., and Bearne, L. (2021).  Effectiveness of web-based and mobile health interventions designed to enhance adherence to physical activity for people with inflammatory arthritis: a systematic review.  Rheumatology advances in practice, 5(1):rkab016.
  * Skivington et al., (2021) Skivington, K., Matthews, L., Simpson, S. A., Craig, P., Baird, J., Blazeby, J. M., Boyd, K. A., Craig, N., French, D. P., McIntosh, E., et al. (2021).  A new framework for developing and evaluating complex interventions: update of medical research council guidance.  bmj, 374.
  * Sniehotta et al., (2014) Sniehotta, F. F., Presseau, J., and Araújo-Soares, V. (2014).  Time to retire the theory of planned behaviour.
  * Swann et al., (2023) Swann, C., Jackman, P. C., Lawrence, A., Hawkins, R. M., Goddard, S. G., Williamson, O., Schweickle, M. J., Vella, S. A., Rosenbaum, S., and Ekkekakis, P. (2023).  The (over) use of smart goals for physical activity promotion: A narrative review and critique.  Health psychology review, 17(2):211–226.
  * Taylor et al., (2022) Taylor, L., Ranaldi, H., Amirova, A., Zhang, L., Ahmed, A. A., and Dibb, B. (2022).  Using virtual representations in mhealth application interventions for health-related behaviour change: A systematic review.  Cogent psychology, 9(1):2069906.
  * Taylor et al., (2013) Taylor, N., Lawton, R., and Conner, M. (2013).  Development and initial validation of the determinants of physical activity questionnaire.  International Journal of Behavioral Nutrition and Physical Activity, 10(1):1–11.
  * Taylor et al., (2019) Taylor, R. S., Long, L., Mordi, I. R., Madsen, M. T., Davies, E. J., Dalal, H., Rees, K., Singh, S. J., Gluud, C., and Zwisler, A.-D. (2019).  Exercise-based rehabilitation for heart failure: Cochrane systematic review, meta-analysis, and trial sequential analysis.  JACC: Heart Failure, 7(8):691–705.
  * Tromp et al., (2022) Tromp, J., Jindal, D., Redfern, J., Bhatt, A., Séverin, T., Banerjee, A., Ge, J., Itchhaporia, D., Jaarsma, T., Lanas, F., et al. (2022).  World heart federation roadmap for digital health in cardiology.  Global Heart, 17(1).
  * van Dis et al., (2023) van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., and Bockting, C. L. (2023).  Chatgpt: five priorities for research.  Nature, 614(7947):224–226.
  * Weidinger et al., (2022) Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022).  Taxonomy of risks posed by language models.  In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 214–229.
  * Zhavoronkov, (2023) Zhavoronkov, A. (2023).  Caution with AI-generated content in biomedicine.  Nature medicine, 29(3):532.
  * Zhou et al., (2023) Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O. (2023).  LIMA: Less is more for alignment.  arXiv preprint arXiv:2305.11206.
  * Zou et al., (2023) Zou, A., Wang, Z., Kolter, J. Z., and Fredrikson, M. (2023).  Universal and transferable adversarial attacks on aligned language models.  arXiv preprint arXiv:2307.15043.