# Simulating Policy Impacts: Developing a Generative Scenario Writing Method
to Evaluate the Perceived Effects of Regulation

Julia Barnett1, Kimon Kieslich2, Nicholas Diakopoulos1

###### Abstract

The rapid advancement of AI technologies yields numerous future impacts on
individuals and society. Policy-makers are therefore tasked to react quickly
and establish policies that mitigate those impacts. However, anticipating the
effectiveness of policies is a difficult task, as some impacts might only be
observable in the future and respective policies might not be applicable to
the future development of AI. In this work we develop a method for using large
language models (LLMs) to evaluate the efficacy of a given piece of policy at
mitigating specified negative impacts. We do so by using GPT-4 to generate
scenarios both pre- and post-introduction of policy and translating these
vivid stories into metrics based on human perceptions of impacts. We leverage
an already established taxonomy of impacts of generative AI in the media
environment to generate a set of scenario pairs both mitigated and non-
mitigated by the transparency legislation of Article 50 of the EU AI Act
(Commission 2024). We then run a user study ($n=234$) to evaluate these
scenarios across four risk-assessment dimensions: severity, plausibility,
magnitude, and specificity to vulnerable populations. We find that this
transparency legislation is perceived to be effective at mitigating harms in
areas such as labor and well-being, but largely ineffective in areas such as
social cohesion and security. Through this case study on generative AI harms
we demonstrate the efficacy of our method as a tool to iterate on the
effectiveness of policy on mitigating various negative impacts. We expect this
method to be useful to researchers or other stakeholders who want to
brainstorm the potential utility of different pieces of policy or other
mitigation strategies.

##  1 Introduction

In addition to creating a suite of positive benefits from facilitating ease of
culturally situated natural language creation and translation (Yao et al.
2023), to assisting with medical imaging (Chen et al. 2022), and empowering
new and accessible forms of education (Alasadi and Baiz 2023), generative AI
has given reason for concern in many aspects of society. Any implementation of
a system using generative AI, or any algorithmic tool for that matter, can
have unintended downstream effects—both positive and negative. It is essential
to have a comprehensive understanding of these potential impacts when
considering how to make decisions regarding the usage of these tools.

Experimenting with deployment of an algorithmic system and experiencing the
tangible impacts can have costly effects on the individuals and society
affected by it. An approach that can address this by helping to develop a
comprehensive understanding of potential impacts prior to deployment is
anticipatory ethics together with participatory foresight (Sarewitz 2011; Brey
2017). Anticipatory ethics guides scientific and technological advancement in
a humane manner by using methodological approaches to consider potential
impacts of the technology at all stages of development (Diakopoulos and
Johnson 2021). Participatory foresight shifts this analysis to include the
many stakeholders affected by the technology rather than simply the algorithm
designer or model deployer (Barnett and Diakopoulos 2022; Bonaccorsi, Apreda,
and Fantoni 2020). This, too, can be costly due to the involvement of a
variety of stakeholders to get a comprehensive understanding of possible
effects. In this work we begin to explore the potential for low cost large
language models (LLMs) to contribute to this method.

Some LLMs, such as GPT-4, have been trained on more data than any human could
consume in a millennium (Achiam et al. 2023). As such, they are able to learn
enough about the world in terms of knowledge and relationships to be able to
(to some extent) simulate some underlying cause and effect relationships in
the world (Li, Nye, and Andreas 2021; Cai, Liu, and Song 2023). We aim to
utilize this underlying ‘world knowledge’ in such models to simulate the
impacts of AI in society given a policy implementation such as a particular
Article of the EU AI Act (Commission 2024). We do so by utilizing written
scenarios as small world representations that reflect and embed social and
causal constraints within their text. Specifically, we prompt GPT-4 to write a
large and varied set of scenarios each in terms of a particular impact about
generative AI (e.g., sensationalism in regards to media quality impacts) in
the media environment to take place in the US in the next five years. We then
introduce a mitigation strategy in terms of a policy implementation, and ask
the model to re-write a given scenario in light of the introduced policy text.
In this study we examine the impact of Article 50 of the EU AI Act about
transparency obligations. To evaluate the quality of the scenarios and measure
the influence of the policy on the impacts represented in the scenarios we
then run a user study where we ask a set of human raters to read both the
original scenario and the re-written scenario and then rate them on four
dimensions that are of high relevance in impact assessment and scenario-
writing literature: severity, plausibility, magnitude, and specificity to
vulnerable populations.

We find that the method we develop to generate written scenarios and re-write
them under a policy condition is effective, producing scenarios that are
largely considered as plausible by our study participants. Moreover, we were
able to collect data to help understand the perceived severity, magnitude, and
specificity to vulnerable populations of the various impacts that were
represented by the scenarios generated. We show that the setup we develop here
is able to demonstrate a difference in perceived impacts when the policy was
simulated as part of the scenario, drawing attention to areas of impact where
the policy may be more or less effective. These findings generally demonstrate
that the approach developed for using LLMs to generate and re-write scenarios
can be a valuable first step for stakeholders wishing to explore policy
options for mitigating impacts, such as policy makers or researchers prior to
more costly evaluation methods such as experiments, or pilot policy
deployment. In our discussion we expand on the capabilities and limitations of
this method and note the areas in which extra human evaluation and monitoring
is needed. By demonstrating the viability of this method, we lay the
groundwork to develop tools for policy makers and other non-technical
stakeholders to more easily identify potential impacts and evaluate policy
proposals as part of an anticipatory governance paradigm.

In sum, our work is primarily composed of the following two contributions: (1)
we develop an approach for the evaluation of policies that may mitigate
societal harms. We use scenarios written by an LLM to convey impacts and then
further use the LLM to simulate an alternative version of the scenario under a
policy condition; and (2) we evaluate the approach with human raters by using
a case study about negative impacts in the domain of generative AI in the
media/info ecosystem and in light of the EU AI Act.

##  2 Related Literature

We now expand on literature in anticipatory impact and governance in order to
frame how our method situates within established work to comprehensively
assess risk and impact of various AI systems. Then we explore existing work
investigating the ability of large language models to learn underlying
relationships of the world to assess how we can leverage these knowledge
representations for the purposes of simulating the effects of policy on
specified harms.

### Anticipatory Impact and Governance

In order to guide the deployment of AI in a beneficial way for individuals and
society, it can be helpful to look ahead and anticipate a wide range of
potential impacts. Recently, the European Union articulated the need for
anticipatory risk management in stating that reasonably foreseeable risks
should be identified before the implementation of AI systems (Commission
2024). Inherent to this call is a need to engage in foresight of how
technology implementation could impact individuals, but also society as a
whole. Consequently, many researchers, NGOs, and companies have engaged in
systematic impact or risk assessment activities to categorize and classify
those impacts (Stahl et al. 2023).

The National Institute of Science and Technologies (NIST) introduced a
structure to improve the trustworthiness of AI that follows a map, measure,
and manage voluntary framework to deploy AI technologies (NIST 2023). They
acknowledge the sociotechnical (Shelby et al. 2023) nature of these risks, and
suggest the use of this framework to both initiate AI risk management and
bolster existing systems. The idea is to (1) map out the potential harms of
these AI systems; this is an active area of research identifying AI harms such
as (Kieslich, Diakopoulos, and Helberger 2023; Kieslich, Helberger, and
Diakopoulos 2024; Barnett 2023; Weidinger et al. 2023; Chanda and Banerjee
2022). The measuring step (2) involves methods to quantify these risks and
enable assessment of whether we are mitigating them successfully. Finally, the
managing step (3) involves integration of responsible policies to prevent
these harms from manifesting. Our proposed method allows stakeholders to
measure harms in an exploratory manner with the goal of enabling people in
positions of power (e.g., legislators and government officials) to introduce
policy to manage these harms.

The goal of algorithmic impact assessment (AIA) is related to the calls for
anticipatory governance studies, namely to identify plausible impacts a
technology might cause in an early development stage (Selbst 2021; Fuerth
2011; Guston 2014). In identifying potential detrimental impacts early,
mitigation strategies can be developed beforehand so that the realization of
those harms can be prevented (Selbst 2021; Guston 2014). Importantly, these
impacts are not only of technical nature, but encompass the sociotechnical
interplay of AI and people (Moss et al. 2021). Thus, anticipating impacts
refers not only to the technological features of AI, but also brings the
challenge of anticipating how different users will utilize it and how that
will scale to societal impacts.

Anticipating these impacts is difficult due to the vast number of deployment
settings and the inherent uncertainty of future developments (Nanayakkara,
Diakopoulos, and Hullman 2020). Though scholars, NGOs, policy-makers, and
companies have deployed various methods to conduct impact assessment, for
instance with literature reviews (Weidinger et al. 2022; Shelby et al. 2023;
Hoffmann and Frase 2023; Bird, Ungless, and Kasirzadeh 2023; Barnett 2023),
the collection of expert opinions (Solaiman et al. 2023) or developer
decisions (Buçinca et al. 2023). While these methods allow for a broad
collection of potential impacts, they are top-down in a sense that they
heavily rely on (domain) expert knowledge, but exclude the view of non-expert
stakeholders. Multiple studies have already pointed out that expert-led
anticipations are prone to be biased, especially towards what they desire the
technology to be capable of (i.e., wishful thinking) (Bonaccorsi, Apreda, and
Fantoni 2020; Brey 2017). Thus, researchers have called for the inclusion of
the voices of laypersons that enrich the current landscape of expert-driven
assessments in providing real-world imaginations of technology impact (Moss et
al. 2021; Metcalf et al. 2021). This form of participatory foresight (Nikolova
2014) also helps in democratizing impact assessment.

Scenario writing is one method that can be an effective approach to stimulate
future thinking of laypeople (Amer, Daim, and Jetter 2013; Amos‐Binks,
Dannenhauer, and Gilpin 2023; Börjeson et al. 2006; Burnam-Fink 2015; Selin
2006). The goal of scenario-writing is to create narratives about future
impact of technology that vividly show how a specific technology impacts the
life of story characters. Numerous plausible and potential futures emerge that
can then be used as a conversation starter to develop mitigation strategies or
to classify user-centric impacts (Amer, Daim, and Jetter 2013; Burnam-Fink
2015). The impact assessment literature has acknowledged the value of
scenarios (Meßmer and Degeling 2023) and several studies have relied on
laypeople to compose future impact scenarios (Diakopoulos and Johnson 2021;
Kieslich, Diakopoulos, and Helberger 2023; Kieslich, Helberger, and
Diakopoulos 2024), use scenarios as a conversation starter for debate about
ethics (Das et al. 2024), or provide situations to collect judgements related
to AI ethics (Awad et al. 2018).

In this study, we combine the potential of scenarios with LLMs and layperson
judgements to help assess impacts. Concretely, we rely on the impact
identification work of Kieslich, Diakopoulos, and Helberger (2023), who
utilized scenario-writing to map potential impacts of generative AI in the
news environment. In our study, we leverage this framework and combine it with
the potential of LLMs to both synthesize written scenarios and, importantly,
to re-write them under a policy condition, allowing us to collect and compare
judgements of the scenarios by people.

### LLMs as Knowledge Representation

Large language models such as GPT-4 (Achiam et al. 2023), Gemini (Team et al.
2023), and Claude 3 (Anthropic 2024) have been trained on enormous quantities
of data from the internet, such as web documents, books, code, images, and
audio. They utilize proprietary datasets as well as open-source web data such
as the continuously growing Common Crawl, which contains more than 250 billion
pages of over a decade’s worth of textual content. While these models
primarily learn how to linguistically represent the way we communicate, they
also learn knowledge representations of how our world operates. OpenAI has
touted the ability of GPT-4 to pass the American Bar Exam with flying colors
(Achiam et al. 2023). Though this has received push back as not being as
successful as originally reported (Martínez 2024), it has still passed well
above the acceptance rate and been shown to outperform lawyers on tasks such
as contract review (Martin et al. 2024).

In addition to performing well on reasoning tasks, LLMs also are capable of
mapping relationships between entities. Park et al. (2023) developed a sandbox
game environment similar to the video game The Sims which was powered entirely
by LLMs. In this simulated environment, generative agents powered by the LLMs
produced believable individual and social behaviors, and knew how to interact
reflecting norms of society. Li, Nye, and Andreas (2021) also assert that
LLMs’ abilities to model language come not only from statistical modelling of
corpora, but also dynamic representations of meaning learned from the training
data.

Even further, there is evidence that LLMs have the ability to map causal
relationships. Long et al. (2023b) explore the ability of GPT-3 to build
causal graphs, determining they can be used to create Directed Acyclic Graphs
(DAGs), though recommend accompaniment by expert knowledge. Long et al.
(2023a) later evaluate the utility of using the LLM itself as the “imperfect
expert” in providing knowledge about the causality of various relationships.
Tu, Ma, and Zhang (2023) used ChatGPT (GPT-3) to explore the LLM’s ability to
identify causal relationships in Neuropathic Pain Diagnosis; they determined
the model served as a good assistive tool for causal discovery, but still
required expert direction. Others have asserted that LLMs can assist with the
advancement of research in causality (Kıcıman et al. 2023). Jin et al. (2023)
go beyond common sense causal understanding and build a dataset to evaluate
LLMs’ ability to perform formal causal and chain-of-thought reasoning; these
tasks prove to be much more challenging for LLMs, though GPT-4 performs the
best out-of-the-box with overall 70% accuracy, second only to their
specifically tuned causal model. Finally, Cai, Liu, and Song (2023)
demonstrate that when LLMs are provided with domain-specific knowledge, they
can produce sound causal reasoning, and can maintain causal reasoning without
this specific knowledge.

We leverage the various forms of knowledge that models such as GPT-4 encode in
this study in order to be able to both generate scenarios reflective of
specific impacts and to then re-write these scenarios given information about
a policy. In this way we are able to simulate variations of scenarios which
make use of the knowledge and understanding of connections and relationships
encoded in the model.

##  3 Methodology

The goal of this work, again, is to understand to what extent LLMs
(specifically GPT-4 in this study) are capable of generating complex scenarios
about a given set of impacts, and their ability to then generate mitigated
versions of those scenarios in light of a specific policy. To implement this
(see Figure 1), we iterated on prompts to generate a dataset of scenarios
reflecting impacts in the media environment due to generative AI. We reviewed
these scenarios to ensure they were adequately representing the impacts for
which we prompted. We then designed a survey to allow respondents to assess
these scenarios across four dimensions that are relevant to risk assessment
and AI ethics (severity, plausibility, magnitude, and specificity to
vulnerable populations). Finally, we aggregated the results across these four
dimensions to demonstrate how to effectively translate the rich textual
scenarios into quantified evaluations.

#### Scenario Generation

Figure 1: Flow diagram of study, starting with prompt engineering to generate
150 scenario pairs (3 scenarios for each of 50 impact types), then manual
author validation check removed 33 scenario pairs (11 impact types), then had
human raters evaluate 117 scenarios, and finally analyzed the aggregated data
across impact themes.

We used GPT-4111Specifically “gpt-4-turbo” available via the OpenAI API which
pointed to the most recent model available as of Feb. 2024. (Achiam et al.
2023) for this study since at the time of data collection it outperformed
other proprietary and open LLMs on the ChatArena benchmark which evaluates
models according to pairwise human comparisons (Chiang et al.
2024)222https://chat.lmsys.org/?leaderboard. We used OpenAI’s API for chat
completions to generate scenarios. To generate scenarios for our study we
start with a set of 50 impact types, organized into 10 higher-level impact
themes, that describe various impacts of generative AI in the media and
information ecosystem (Kieslich, Diakopoulos, and Helberger 2023). Future work
may consider extending the methods developed here to other impact typologies
and domains. We next describe how and why we arrived at our prompting approach
and then elaborate on how we generated scenarios to support our study.

Developing a Prompting Approach. We initially experimented with simply
prompting the model with the 10 general impact themes in order to understand
the diversity with which GPT-4 could uncover potential impacts when only
prompted with a general impact theme. We thus prompted GPT-4 to generate
scenarios for each of the ten impact themes ten times each: autonomy,
education, labor, legal rights, media quality, political, security, social
cohesion, trustworthiness, and well-being. In the Appendix, Figure 5 displays
the number of specific impact types that were uncovered by the LLM, as well as
how often (in the ten runs) various specific impact types were discussed.

For all themes except trustworthiness, when using this prompting approach the
model was not able to generate scenarios reflecting each of the specific
impact types in the typology outlined by Kieslich, Diakopoulos, and Helberger
(2023). More importantly, for each of the impact themes, the model tended to
heavily skew toward discussing a couple of specific impact types and rarely
touched on others. For instance, prompting only with the impact theme of well-
being produced scenarios covering three out of four specific impact types from
the typology, and generated scenarios discussed mental harm 90% of the time,
never mentioned physical harm, and only discussed addiction and reputation two
out of ten times each.

Due to this skew and non-comprehensive spread of impact types present in the
scenarios when prompting only with impact theme, we moved on to experiment
with prompting the model explicitly for each of the 50 impact types rather
than at the level of the ten impact themes. It became evident that utilizing
LLMs to generate scenarios was more effective when done with a human-in-the-
loop providing contextual knowledge (in this case, the full taxonomy provided
by Kieslich, Diakopoulos, and Helberger (2023)) in order to guide creation of
specific outputs—if we only used general impact themes we would not uncover as
many impacts or the same level of detail. Thus, for the remainder of this
paper, we evaluate scenarios generated by GPT-4 that were generated with
inclusion of each specific impact type.

Creating Stimulus Material for the Study. For each specific impact type, we
generated scenario pairs consisting of one illustrative version ($S$) to
narrativize the impact and a corresponding policy-mitigated version
($S^{\prime}$) which was re-written in light of a policy condition. We chose
to generate 3 scenario pairs for each impact type in order to evaluate the
reliability of the model for generating scenarios relevant to the impact while
also providing some variance in the sample. This process resulted in the
production of 150 scenario pairs. In order to remain consistent across all
impact types, the only variation in the prompt was switching out the words for
the impact type using the format “$\langle$specific impact type$\rangle$ in
regard to $\langle$impact theme$\rangle$” (e.g., “sensationalism in regards to
media quality” or “polarization in regard to social cohesion”); all other
aspects of the prompt remained the same. The full prompts can be found in the
Appendix Section A.2.

We refer to the prompt for the illustrative scenario, $S$, as Prompt 1, and
the prompt for the policy-mitigated scenario, $S^{\prime}$, as Prompt 2.333For
each scenario pair we utilized a fresh state of the chatbot with temperature =
0.7. For Prompt 1, We first included context to define what a scenario was,
name the specific impact type in the context of the impact theme, and define
generative AI. We then directed the model to write a short fictional scenario
set in the United States in the year 2029 about this impact, and included
specific instructions for the model not to introduce reflection or meta
analysis in the scenario since we reasoned that this might influence or bias
the interpretation of participants in our study. For Prompt 2, we indicated to
the model that a piece of legislation was enacted and that it should rewrite
the scenario in light of that legislation. For this study, we provided it the
exact text of the final version of Article 50 of the EU AI Act (Commission
2024), which concerns transparency obligations for certain AI systems,
including for general purpose AI models.

#### Scenario Validation

To validate the scenarios for the study, two of the authors independently
evaluated the 150 scenarios in order to assess whether the scenarios actually
depicted the designated impact provided in the corresponding prompt.
Evaluators rated each scenario as (1) depicting the impact, (2) somewhat
depicting the impact, and (3) not depicting the impact. A scenario pair needed
either both evaluators to assess it as depicting the impact or one evaluator
saying it depicted and the other saying it was somewhat present to be
considered valid. If zero or only one of the three scenarios were deemed
valid, the specific impact type was removed from further evaluation. If two of
the three scenarios passed the validation check, the third scenario was
regenerated and then reassessed by both evaluators to ensure all three
scenarios depicted the designated impacts.

After this process, we ended up with 39 of 50 of the original impact types and
thus 117 scenario pairs for evaluation in our user study.

#### Study Participants

For this study we recruited raters for the scenarios on Prolific that had an
approval rating of at least 95/100, and 100 approved tasks. To ensure
evaluators were familiar with the context of the scenarios (which all took
place in the United States), we utilized evaluators who were proficient in
English and resided the United States.

We excluded respondents that failed at least two attention checks. Our
attention checks took the form of nonsensical statements for which there was a
logical and common sense response (e.g., To what extent do you agree with this
statement: “I’ve developed a way to photosynthesize, so I sustain myself on
sunlight alone, like a plant.”? This is to check your attention.)444See:
Prolific’s Attention and Comprehension Check Policy.
https://tinyurl.com/prolific-attention ; Our attention checks are available
at: https://tinyurl.com/attention-checks. Evaluators were not allowed to take
the survey multiple times. Fifteen raters were removed via attention checks
(6% of total 249 recruited) resulting in a final participant pool of 234
raters total. 12% of evaluators did not consent to demographic data collection
through Prolific, but the remaining sample was comprised as follows: a fairly
equal dispersion of male and female identifying evaluators (51% F; 48% M; 1%
prefer not to say), predominantly white (68%), followed by 11% Mixed, 8%
Black, 7% Asian, and 5% other; and an average age of 38 (minimum: 19, maximum:
76).

We had six people evaluate each scenario pair in order to establish an average
response across multiple independent raters who might each bring their own
subjectivities to their ratings. We paid each evaluator to annotate a random
set of three scenario pairs, which took approximately 14 minutes (final study
median). We paid $3.25 for each completed set (an estimated pay of $14 per
hour).

#### Survey Design

We chose to assess the content of scenarios across four dimensions to assess
the impacts portrayed: severity, plausibility, magnitude, and specificity to
vulnerable populations.

The EU AI Act (Commission 2024) defines risk as “the combination of the
probability of an occurrence of harm and the severity of that harm”
positioning both prevalence and severity as key dimensions that need to inform
a risk-based approach to addressing AI impacts in society. Assessing the
severity and prevalence (what we term magnitude in this study) of impacts
presented in scenarios is thus a crucial step to establishing mitigation
priorities for AI systems, such as general purpose models that can cause a
wide range of different harms (Meßmer and Degeling 2023).

Plausibility is an essential evaluation dimension for risk analysis (Glette-
Iversen, Aven, and Flage 2022) and scenario quality (Amer, Daim, and Jetter
2013). Following the typology consolidated by Börjeson et al. (2006), we are
generating explorative scenarios in this work. As such we need a measure to
assess the degree to which the events in a scenario are realistic or at least
consistent with known social dynamics and forces such that one could
reasonably conclude that a scenario could happen based on their world
knowledge (Urueña 2019).

Finally, assessing specificity to vulnerable populations is not an established
metric for evaluating scenarios, but it is widely understood that there is
often a disparate impact on people not belonging to the majority power group
when it comes to societal harms, including AI harms which can be magnified for
vulnerable and minority groups (Benjamin 2019; Hagerty and Rubinov 2019;
Shelby et al. 2023).

In our study we presented evaluators with a scenario (either $S$ or
$S^{\prime}$), and asked them to answer the following questions with respect
to the scenario they had just read:

  1. 1.

“Severity” is defined as “ the condition of being very bad, serious,
unpleasant, or harsh”. Please rate the severity of this scenario on a scale of
1 (not severe) to 5 (extremely severe).

  2. 2.

“Plausibility” is defined as being reasonable to conclude something may
happen. Please rate the plausibility of this scenario on a scale of 1 (not
plausible) to 5 (extremely plausible).

  3. 3.

“Magnitude” is defined as “great size or extent.” Please rate the magnitude by
considering the following: This scenario is an example that could happen to
any number of people in the world. How many people do you think could be
affected by the harms presented in this scenario? 1 (a small number of
people); 5 (the majority of people in society).

  4. 4.

“Vulnerable Populations are defined as “individuals who are at greater risk of
poor physical and social health status”. Please rate how specific you think
the risk(s) presented in this scenario are to vulnerable populations in
comparison to non-vulnerable populations. 1 (not specific to vulnerable
populations); 5 (extremely specific to vulnerable populations).

We intentionally wanted to allow evaluators’ intuition to guide them to
evaluate these rather than have any sort of anchoring guide such as giving an
explicit example of what would constitute ‘extremely severe’. The definitions
of the items were developed from dictionary definitions in order to elicit
ratings from colloquial understandings, rather than injecting expert
definitions and biases into the evaluations. After a rater evaluated the first
scenario, they were then presented with “a re-written version of the above
scenario” (either $S$ or $S^{\prime}$, whichever they did not see first), and
performed the same evaluation on this second scenario. To mitigate the
potential for ordering effects we randomly counterbalance the order of
presentation: three evaluators saw each scenario pair in the order $S$,
$S^{\prime}$, and three people saw the scenario in the order $S^{\prime}$,
$S$. We later performed a paired sample t-test to assess if there were any
ordering biases and found none overall or when aggregated by impact type.

Figure 2: Bar chart displaying from top to bottom the four dimensions:
severity, plausibility, magnitude, and specificity to vulnerable populations.
Left plot: we first display the mean raw scores ($M_{S}$ and $M_{S^{\prime}}$)
for each impact theme (in alphabetical order: overall, autonomy, education,
labor, legal rights, media quality, political, security, social cohesion,
trustworthiness, and well-being). Right plot: we display the deltas
($M_{S}-M_{S^{\prime}}$) for each theme. For significance levels we include
above the bars: * for $p\leq 0.05$, ** for $p\leq 0.01$, and *** for $p\leq
0.001$.

##  4 Analysis and Results

We now evaluate the efficacy of GPT-4 as a scenario generator and iteration
tool for simulating potential policy mitigation by using a case study in the
domain of AI in the media ecosystem with Article 50 of the EU AI Act
(Commission 2024). As detailed above in the methodology, we conducted a user
study to evaluate GPT-4 generated scenario pairs comprised of: ($S$) a
scenario illustrating a specified negative impact in the media environment and
($S^{\prime}$) a re-written version of that scenario that assumes the
transparency legislation described in Article 50 of the EU AI Act was enacted.

For each impact theme below, we will examine (a) which scenarios GPT-4 was
adequately able to generate when specifically prompted, (b) the human user
study raw scores to understand how these impacts were perceived, and (c) the
delta between $S$ and $S^{\prime}$ to evaluate whether humans perceived the
policy to be effective at mitigating the designated harm. We begin by
examining overall significant trends, then proceed alphabetically by impact
theme. All references to significance refer to results from paired sample
t-tests, and we report significance at levels of $p<0.05,0.01$, and $0.001$.
All references to a taxonomy of impacts of generative AI in the media
environment uncovered by human authors refer to (Kieslich, Diakopoulos, and
Helberger 2023).

#### Overall (Figure 2; Table 1)

We first describe some high level findings from the user study. Of the 50
specific impact types from the established taxonomy of generative AI impacts
in the media environment, GPT-4 was able to adequately depict 39 impacts when
prompted specifically. This indicates that there are some areas where the
prompting approach taken was not successful, and future work could explore
prompts that define impact types more explicitly.

Evaluators generally thought the impacts depicted in these scenarios were
relatively severe ($M_{S}=3.66;SD_{S}=1.08$), but mitigated in part by the
introduced legislation ($M_{\Delta}=-0.38$***; $SD_{\Delta}=1.01;p<0.001$),
with $40\%$ of scenarios demonstrating a lower severity after the transparency
legislation was introduced. They found them to be quite plausible
($M_{S}=3.94;SD_{S}=0.96$), with the legislation doing little to affect the
plausibility ($M_{\Delta}=-0.03$*; $SD_{\Delta}=0.84;p<0.05$). Evaluators
believed that the original scenarios had impacts affecting a greater number of
people ($M_{S}=3.79;SD_{S}=1.00$) than the policy mitigated scenarios
($M_{\Delta}=-0.23$***; $SD_{\Delta}=0.90;p<0.001$), indicating they thought
that when transparency legislation was introduced the harm affected a smaller
amount of people. Finally the human evaluators at large had fairly mid-range
assessments of specificity to vulnerable populations
($M_{S}=3.06;SD_{S}=1.24$), indicating that though these were slightly more
impactful to vulnerable populations, they were still having an impact on the
majority of society. This perception also lessened with introduced policy
($M_{\Delta}=-0.13$***; $SD_{\Delta}=0.80;p<0.001$), though this was a
relatively smaller change in comparison to severity and magnitude.

For each of the four dimensions (severity, plausibility, magnitude, and
specificity to vulnerable populations), the changes within the overall
comparison of $M_{S}$ and $M_{S^{\prime}}$ were all significant at $p<0.001$.
In Figure 2 we display the significance of all impact themes. Within severity
there were several statistically significant results, on which we elaborate
below. None of the changes at the impact theme level in plausibility were
statistically significant, which speaks to the perceived possibility of both
versions of the scenario transpiring in the near future. Only labor, media
quality, and trustworthiness had statistically significant changes in
magnitude, and statistically significant changes in perceived specificity to
vulnerable populations were only present in impacts regarding political and
well-being.

Now we will drill into the specific impact themes, referencing individual
specific impact types (Appendix Tables 2-11) when relevant. We note
significance levels when present, otherwise discussing general trends.

#### Autonomy (Table 2)

From the taxonomy of generative AI impacts, there were three main impact types
within autonomy: loss of control, loss of orientation, and machine autonomy.
Using tailored prompting, we were only able to adequately generate relevant
scenarios for loss of control and loss of orientation. The scenarios all
discussed the impacts in the context of information consumers, with loss of
orientation also focusing on the impact on journalists. Overall, evaluators
perceived the transparency legislation to be effective at lessening the
severity of these autonomy impacts ($M_{\Delta}=-0.72$*;
$SD_{\Delta}=1.15;p<0.05$) and thought they were highly plausible
($M_{S}=3.94;SD_{S}=0.96$).

Evaluators thought the scenarios depicting loss of control were much more
severe ($M_{S}=4.22$) than those depicting loss of orientation ($M_{S}=3.33$),
with autonomy impacts having an average severity score of $M_{S}=3.78$.
However, loss of control scenarios depicted impacts perceived to be heavily
mitigated by transparency legislation: these re-written scenarios had on
average $M_{\Delta}=-1.00$, one of the greatest deltas among all impact types.
For instance, in one of the scenario pairs a reader named Mary felt out of
control in her own ability to leave the highly partisan echochamber of
political information encompassed by each news story she encountered. However,
the legislation mandating that each AI-story be marked as “AI-generated”
allowed her to question the content and intentionally find some human-authored
articles with opposing arguments, ultimately leaving her in charge of her own
opinions. Evaluators on average rated this as much less severe than the non-
mitigated scenario ($M_{S}=4.67;SD_{S}=0.47$;
$M_{\Delta}=-2.00;SD_{\Delta}=0.82$).

Evaluators believed these issues affected a large portion of society
($M_{S}=4.06;SD_{S}=0.97$) with the second highest average scores for
magnitude. They believe the mitigated impacts affected slightly fewer people
($M_{\Delta}=-0.17;SD_{\Delta}=1.01$), and similarity the mitigation made the
impacts less specific to vulnerable populations
($M_{\Delta}=-0.44;SD_{\Delta}=0.83$).

#### Education (Table 3)

Though when generally prompted for education impacts GPT-4 struggled to
produce relevant scenarios (20% of the time; Figure 5), it was adequately able
to generate relevant scenarios when prompted specifically for critical
engagement and literacy impacts. One scenario each had to be regenerated for
these specific impacts—these two scenarios both discussed fake news about
schools, not about actual education impacts. The 6 scenario pairs within
education all focused on readers and consumers of news media, typically within
a K-12 school environment. None of the changes between the illustrative and
policy-mitigated scenarios for education impacts were significant.

#### Labor (Table 4)

There are five specific impact types within the labor theme: changing job
roles, competition, job loss, loss of revenue, and unemployment. Of these, the
only specific impact type for which it was difficult to successfully generate
scenarios was loss of revenue—this was the only impact type which took many
additional attempts to generate relevant scenarios. All labor scenarios almost
exclusively focused on the lives of journalists, however some scenarios
detailing changing job roles described the effects on readers and consumers of
news media, and loss of revenue detailed the effects on news organizations as
a whole.

On average, labor impacts were seen as the third most severe (only less than
political and well-being impacts). Even further, the transparency legislation
was perceived as having a statistically significant effect on the severity of
these impacts ($M_{\Delta}=-0.63$***; $SD_{\Delta}=1.11;p<0.001$). Similarly,
respondents perceived the mitigated scenarios as impacting a smaller subset of
society ($M_{\Delta}=-0.28$*; $SD_{\Delta}=0.80;p<0.05$).

The most severe of the impact types were changing job roles and unemployment,
with severity scores of $M_{S}=4.30$ ($SD_{S}=0.90$) and $M_{S}=4.22$
($SD_{S}=0.63$), respectively. However, transparency legislation seemed to
have a large impact on changing job roles, job loss, and unemployment
($M_{\Delta}=-1.1$*, $-0.78,$ and $-0.56$), indicating that transparency could
potentially lessen the perception of impacts in these areas. Labor impacts
were seen as the most plausible ($M_{S}=4.17;SD_{S}=0.79$) on average of all
impact themes, however the policy-mitigated scenarios were perceived as less
plausible ($M_{S^{\prime}}=-0.26;SD_{S^{\prime}}=0.88$).

#### Legal Rights (Table 5)

Perhaps the most elusive of all impact themes, we were only able to adequately
generate relevant scenarios for one of four impact types for legal rights:
copyright issues. Freedom of expression, lack of regulation, and legal actions
all produced scenarios detailing other types of impacts. Thus, when we discuss
the impacts of legal issues we are exclusively discussing copyright issues.
These scenarios all described AI news generators unknowingly stealing or
leaking copyrighted content unbeknownst to journalists or the creators of said
content.

Legal rights impacts, though relatively severe ($M_{S}=3.78;SD_{S}=0.63$), had
the largest perceived decrease in severity due to transparency legislation
($M_{\Delta}=-0.89$*; $SD_{\Delta}=0.87;p<0.05$). They were seen as reasonably
plausible ($M_{S}=3.67;SD_{S}=0.47$), with a slight increase in plausibility
when legislation mitigated the harm ($M_{\Delta}=0.11;SD_{\Delta}=0.74$). They
seemed to affect a moderate portion of society ($M_{S}=3.44;SD_{S}=0.68$) and
with moderate to low specificity to vulnerable populations
($M_{S}=2.89;SD_{S}=0.99$) with no change to magnitude or specificity to
vulnerable populations when the transparency legislation was introduced.

#### Media Quality (Table 6)

The most prolific of all impact themes, there were 16 original impact types
within media quality of which we were able to prompt GPT-4 to generate 12:
accuracy/errors, clickbait, credibility/authenticity, distinction between
journalism and ads, ethics, journalistic integrity, lack of diversity/bias,
lack of fact-checking, loss of human touch, over-personalization,
sensationalism, and superficiality. We were unable to generate relevant
scenarios for accountability, attribution, explainability, and reframing,
which we hypothesize happened due to the intangibility and relative
abstraction of these impact types. Typically in these instances the scenarios
reverted to discussing fake news/misinformation and accuracy/biases—impacts we
specifically prompted for elsewhere. The media quality scenarios focused
mostly on digital news outlets, with the generative AI typically taking the
form of a news generator. The impacts focused on both journalists and
consumers of news media.

Transparency legislation was perceived as having a statistically significant
effect on the severity of these impacts ($M_{\Delta}=-0.34$**;
$SD_{\Delta}=1.16;p<0.01$), as well as affecting fewer people when mitigated
by the transparency legislation ($M_{\Delta}=-0.31$**;
$SD_{\Delta}=1.05;p<0.01$). The transparency legislation can therefore be seen
as particularly relevant to impacts relating to this theme.

Within media quality, the specific impact types with the highest severity were
sensationalism, clickbait, credibility/authenticity, and lack of fact-checking
with scores of $M_{S}=4.38,4.00,3.90$, and $3.78$, respectively. The impact
types for which the transparency legislation had the strongest mitigation
effect were journalistic integrity, clickbait, ethics, and lack of fact-
checking, with $M_{\Delta}=-0.89$*, $-0.88,-0.88$, and $-0.78$, respectively.
On average media quality impacts were perceived as very plausible
($M_{S}=4.00;M_{SD}=0.98$), with legislation mitigation only slightly
affecting the plausibility ($M_{\Delta}=-0.14;SD_{\Delta}=0.84$). The specific
impact types that were perceived as impacting a relatively substantially large
population of people were clickbait, sensationalism, and lack of
diversity/bias ($M_{S}=4.38,4.25$ and $4.10$, respectively).

#### Political (Table 7)

Each of the specific impact types within political impacts were successfully
generated by GPT-4: fake news/misinformation, manipulation, opinion monopoly,
and political consequences. This was the impact theme that specifically
affected public figures (mainly politicians) more so than any other impact
theme, but the scenarios also focused on the effects on society.

Political impacts across the board tended to be perceived as the most severe
of all impact themes ($M_{S}=4.08;SD_{S}=0.76$), with fake news/misinformation
having the highest severity ratings of all impact types with a score of
$M_{S}=4.56$ ($SD_{S}=0.50$), and manipulation not trailing too far behind
($M_{S}=4.22;SD_{S}=0.92$). On average, transparency legislation seemed to
mitigate the severity ($M_{\Delta}=-0.56$***; $SD_{\Delta}=0.86;p<0.001$) of
political impacts and even more so in fake news/misinformation
($M_{\Delta}=-0.78$*; $SD_{\Delta}=0.63;p<0.05$) and manipulation
($M_{\Delta}=-0.67$*; $SD_{\Delta}=0.82;p<0.05$). One of the scenarios
portraying fake news detailed an entirely fabricated political scandal
concocted by an AI news generator and widely disseminated to the public that
ultimately led the political candidate to drop out of the race. In the
transparency mitigated scenario, an “AI-generated” tag was attached to the
piece, and news consumers all paid attention to the human-verified fact
checking of the inaccuracies of the purported scandal.

These scenarios detailing political impacts were seen as very plausible
($M_{S}=4.11;SD_{S}=0.97$), second only to labor scenarios. They also notably
were one of the only impact themes for which the transparency legislation had
a perceived significant impact on the specificity to vulnerable populations
($M_{\Delta}=-0.25$*; $SD_{\Delta}=0.68;p<0.05$), indicating that the
perceived effect of this legislation may be even more beneficial to those more
disparately affected by AI harms.

#### Security (Table 8)

Though GPT-4 only adequately detailed security impacts half the time when
prompted generally with the general security impact theme—and only hacking, at
that—it was able to consistently generate scenarios for cybersecurity and
hacking when specifically prompted. These scenarios tended to focus on public
figures (politicians, celebrities, business people, etc.) as victims of
attacks and public perception as a result of hacking and cybersecurity
breaches. There were no significant perceived changes among any of the four
dimensions in security impacts.

#### Social Cohesion (Table 9)

Of all the specific impact types within social cohesion, we were able to
successfully prompt GPT-4 to generate scenarios for four out of five:
dissatisfaction, polarization, real-world conflicts, and social divide.
Interestingly, the only impact type we were unable to generate scenarios for
was discrimination—this should be a straightforward generation and we
hypothesize this could have been an issue with content moderation preventing
these topics from being discussed in the scenarios. Social cohesion scenarios
focused more on the readers than any other category—all other impact themes
had at least a minor focus on journalists or public figures. All focused on
some degree of polarization, even when that was not the specific prompt.

On average, impacts within social cohesion tended to be less severe than other
impacts detailed in the taxonomy ($M_{S}=3.33;SD_{S}=0.94$), with the
exception of polarization ($M_{S}=3.78;SD_{S}=1.03$). There were no
significant perceived changes within social cohesion impacts.

#### Trustworthiness (Table 10)

We were able to generate adequately relevant scenarios for each of the impact
types within trustworthiness: discernment of fact and fiction, information
chaos, media fatigue, mistrust, and overreliance on AI. These scenarios tended
to focus on the relationship between readers and journalists or readers and
the news environment at large. They also tended to feature a trajectory
towards a lack of trust between society and the news we consume.

Though comparatively less severe than other impact themes pre-policy
mitigation ($M_{S}=3.44;SD_{S}=1.13$), legislation had a statistically
significant perceived effect on lessening this ($M_{\Delta}=-0.36$*;
$SD_{\Delta}=0.90;p<0.05$), and similarly lessened the perceived amount of
people impacted ($M_{\Delta}=-0.29$*; $SD_{\Delta}=0.81;p<0.05$).

All specific impact types within trustworthiness had extremely similar scores,
with the exception of overreliance on AI affecting the most people
($M_{S}=4.33;SD_{S}=0.47$), and media fatigue tending towards being perceived
as more severe when transparency legislation was introduced
($M_{\Delta}=+0.22;SD_{\Delta}=0.63$). Intuitively this makes sense, since
labeling media as AI-generated creates another layer of information for people
to process and make sense of and which therefore may contribute to fatigue.

#### Well-Being (Table 11)

Of the four specific impact types identified by the taxonomy for well-being,
GPT-4 only adequately generated relevant scenarios for addiction and mental
harm. It was unable to generate scenarios when prompted specifically for
reputation impacts, which is interesting given that this harm was present in
other scenarios, such as when prompting for political and media quality
impacts. Each time we prompted the LLM to generate scenarios for physical
harm, it defaulted to discussing mental harms such as anxiety and paranoia,
which can manifest physically but we classified as mental harms for the
purpose of this study. Scenarios involving addiction focused on the addictive
nature of generative media due to extreme personalization and proliferation.
Mental harms as noted focused on the resulting anxiety and other mental health
concerns arising from generated content, constant deluge of news media
(whether fact or fiction), and impact of fake news and misinformation on the
psyche.

Well-being impacts had the second highest severity ranking
($M_{S}=3.94;SD_{S}=0.91$), second only to political harms. Transparency
legislation was also perceived to be effective at reducing this severity
($M_{\Delta}=-0.44$*; $SD_{\Delta}=0.76;p<0.05$). Addiction was perceived as
slightly more severe than mental harm ($M_{S}=4.11;SD_{S}=0.87$ vs.
$M_{S}=3.78;SD_{S}=0.92$, respectively), though the transparency legislation
had a tendency to mitigate both even if this pattern did not reach statistical
significance ($M_{\Delta}=-0.67;SD_{\Delta}=0.82$ and
$M_{\Delta}=-0.22;SD_{\Delta}=0.63$, respectively). Legislation in this impact
area also seemed to have a significant effect on reducing the specificity to
vulnerable populations ($M_{\Delta}=-0.44$*; $SD_{\Delta}=0.76;p<0.05$).

##  5 Discussion and Further Work

In this work we developed a method utilizing LLMs to generate scenarios to
evaluate policies that may mitigate negative impacts on society. We
demonstrate this method by simulating scenarios both mitigated and non-
mitigated by transparency legislation—Article 50 of the EU AI Act—to convey
negative impacts of generative AI in the media ecosystem. We then asked human
evaluators to rate these simulated futures in order to gauge human perception
of the severity, plausibility, magnitude, and specificity to vulnerable
populations of this policy mitigation. We believe this method offers an
opportunity to conduct policy evaluation on a given set of impact types in a
straightforward manner. We now recap our high level findings, discuss
limitations and capabilities of this method, elaborate on immediate
possibilities of future research, and situate this method in the context of
risk-assessment work.

Our study follows the goal of algorithmic impact assessment studies as it aims
to map negative impacts of a given technology and explore mitigation
strategies in how to prevent them (Selbst 2021; Moss et al. 2021; Guston
2014). Using scenario-writing as a bottom-up approach of illustrating
detrimental impacts of AI technology (Amer, Daim, and Jetter 2013; Meßmer and
Degeling 2023; Börjeson et al. 2006), we supplement the top-down driven
landscape of AIA. In this study, we introduced LLMs as a cost-effective way to
create noteworthy scenarios based on impact themes identified in a study with
human respondents. To demonstrate this method, we evaluated 10 impact themes
established in prior work by Kieslich, Diakopoulos, and Helberger (2023) as
relevant to the impact of generative AI on the media and information
ecosystem: autonomy, education, labor, legal rights, media quality, political,
security, social cohesion, trustworthiness, and well-being. Plausibility
scores for the scenarios we generated remained relatively high across the
board, indicating that GPT-4 was able to produce scenarios of impacts that
were perceived to be genuinely possible depictions of the near-future impact
of generative AI in the information ecosystem. The introduced transparency
legislation was perceived to mitigate depicted negative impacts (whether in
terms of severity, magnitude, or specificity to vulnerable populations) with
statistical significance for autonomy, labor, legal rights, media quality,
political harms, trustworthiness, and well-being. It did not seem to be
impactful in the areas of education, security, or social cohesion, suggesting
that perhaps other policy interventions are needed to combat negative impacts
in these areas. For one impact, media fatigue, data suggests that the policy
could even increase the severity of the impact. There were only two areas in
which the legislation was perceived as statistically significantly reducing
the specificity of the harms for vulnerable populations: political and well-
being negative impacts, although overall the impact types that we studied here
were not perceived to be highly specific to vulnerable populations.

This study demonstrates that LLMs can sufficiently simulate complex narratives
about AI’s impact and mitigation strategies, further contributing to the
ongoing exploration of the ability of LLMs to map underlying complex
relationships of the world, adding to discourse amongst Park et al. (2023);
Tu, Ma, and Zhang (2023); Kıcıman et al. (2023); Cai, Liu, and Song (2023).
These results generally demonstrate that our approach for using LLMs to
generate scenarios, use those scenarios to simulate new scenarios in light of
a policy, and then evaluate perceptions of impacts using human raters, can be
valuable for understanding the degree to which the policy may be effective and
help understand trade-offs in how the policy relates to different impacts.
Being able to rank the impacts that may be shifted as a result of a proposed
policy could be used to then allocate scarce experimental resources towards
verifying the potential of a policy to mitigate an impact, such as through
policy pilot programs. Additionally, based on our findings, future work might
devise concrete measures for specific, more severe, impacts and then measure
them in the years before and after the EU AI Act comes into force.

In this study we only explored one part of one piece of legislation, but other
policy proposals such as US President Biden’s Executive Order on AI (Biden
E.O. 14110 of Oct 30, 2023) or the Chinese Generative AI Regulation (of China
No. 15, July 13, 2023) offer interesting potential points of comparison.
Future work will need to extend this method to account for more complex
policies as well as to consider how different policies may interact to produce
impacts on different stakeholders in society (and in different societies). The
scenarios we generated also largely represent a best-case situation for policy
impact as they largely assume that the policy being evaluated would be
effectively enforced and upheld, yet it is possible that only certain elements
of the legislation would be adhered to or properly enforced.

While we demonstrate this method with respect to negative impacts of
generative AI in the news ecosystem, future work can be done to demonstrate
generalizeability to other AI ethics harms, or harms even completely outside
of AI. Through our experimentation, we discovered that utilizing our method
alongside a human-made taxonomy of outlined harms provide for a more
comprehensive understanding than allowing the LLM to come up with the harms
itself. This comes against the cost of having a typology ready to go, but we
found it was helpful for overall generation to ensure diverse impact themes
emerged. Thankfully there are already rich typologies of harms of all sorts
such as sociotechnical harms of algorithmic systems (Shelby et al. 2023),
risks of harm from language models (Weidinger et al. 2021), or harms of
generative audio models (Barnett 2023). Future work should also be conducted
to assess the generalizeability of this method—for instance, when harms are
extremely niche and not widely discussed in training datasets it is unclear
whether LLMs may struggle to generate relevant scenarios for these types of
impacts.

Our implementation of the method also had a specified timeframe: five years in
the future. We do not know how plausible the scenarios would be at more
immediate time intervals (e.g., 6 months to 1 year) or further out in the
future. It’s possible the scenarios could take on more of a science fiction
element if we allowed for longer time intervals, which could potentially
diminish the plausibility of scenarios and thus their relevance to policy
evaluation. Additionally, we did not specify the level of jurisdiction for
which the legislation would be enacted and enforced—it could have differing
effects if implemented at a local versus federal or global level or according
to the specific policy environment or set of institutional actors in a
jurisdiction, but we did not specify this in our generated scenarios.

We further position the method established in this work with respect to the US
NIST Risk Management Framework (NIST 2023)—a structure that organizations can
use to facilitate risk management of design, development, and deployment of AI
tools. The framework organizes risk management into three steps: mapping,
measuring, and management. Creating the typologies, such as the one utilized
in this paper (Kieslich, Diakopoulos, and Helberger 2023) constitute the
mapping step, and then this method introduces a way to both measure the
(perception of) severity and magnitude of these impacts to allow for
stakeholders in power (e.g., legislators, government officials, AI
researchers) to manage these harms. This method can assist with the
prioritization of managing these harms by exploring the effectiveness of
potential mitigation strategies which assists in deciding which risks to
address first. Measuring severity, plausibility, magnitude, and specificity to
vulnerable populations can also inform mitigation strategies beyond policy
intervention. Our method is straightforward to adapt and can be extended and
tested in new domains.

#### Specific Limitations of GPT-4

We want to further emphasize the limitations of this work that are derived
from the use of GPT-4. Of the 50 impact types outlined by Kieslich,
Diakopoulos, and Helberger (2023), we were only able to generate 39 utilizing
our prompting method (Appendix A.2). When we framed the prompts more broadly
in terms of the ten impact themes, the generated scenarios focused on a
heavily skewed set of impact types (see Figure 5 in the Appendix A.3); for
instance, well-being harms focused almost entirely on mental harm with little
acknowledgement of addiction, physical harm, or reputation. Finally, we were
entirely unable to generate some harms (e.g., discrimination), which we
hypothesize could be due to the highly tuned nature of this model to prevent
toxicity being present in outputs. There will be biases present in all
LLMs—both due to tuning and training data. Future work needs to explore how
those biases may be reflected in scenarios and in models’ simulation of how a
policy might impact a scenario.

##  6 Conclusion

In this work we develop and demonstrate the viability of a methodology to
evaluate the potential mitigation effect of polices on given societal harms.
We did so by using a large language model (specifically GPT-4) to generate
scenarios in the near future both mitigated and non-mitigated by a given
policy and then evaluate the perception of severity, plausibility, magnitude,
and specificity to vulnerable populations. This methodology can support
anticipatory governance approaches and risk management frameworks by lowering
the cost for exploring policy options to mitigate harms from established
taxonomies of impact. In addition it can help orient attention towards
promising policy options where more rigorous or longitudinal evaluation is
warranted and could furthermore be useful to help brainstorm the potential of
different policies or mitigation strategies. Limitations of the method as well
as a host of future work which can expand on the approach are discussed.

##  Appendix A Appendix

###  A.1 Example Scenario Pair ($S$ and $S^{\prime}$)

Below is an example scenario pair for accuracy and errors within media quality
harms.

Figure 3: Example scenario ($S$) for accuracy and errors within media quality.

Figure 4: Example re-written scenario ($S^{\prime}$) for accuracy and errors
within media quality after transparency legislation was introduced.

###  A.2 Prompts

Prompt 1: Prompt for scenario $S$:

“A scenario is a short story that includes a setting of time and place,
characters with particular motivations and goals, and a plot. A scenario must
be written in a narrative form and not divided into different sections. A
scenario is descriptive and should leave the interpretation up to readers, and
thus does not include any analysis either at the end or throughout. Therefore,
this writing task asks for a scenario that proposes no solutions but focuses
on descriptive situations regarding the issue of $\langle$specific impact
type$\rangle$ in regard to $\langle$impact theme$\rangle$ due to the
deployment of generative AI technology in the news environment without
proposing new policies to mitigate the harm.

Generative AI refers to a technology that can create new content (e.g., text,
images, audio, video) based on the content it was trained on.

Write a short ($\sim$300 word) narrative fictional scenario taking place in
the United States in 2029, or about 5 years in the future. This story should
depict the risks or negative impacts deriving from this AI deployment in the
media environment and should not try to resolve those impacts. Concentrate on
the narrative style and the characters in the story.

DO NOT add your own analysis or conclusion to the text.

DO NOT end the scenario with a meta analysis about takeaways for the reader.
Let it be up to interpretation.

DO NOT state the problem or resulting harms explicitly.

DO NOT have the characters reflect on the implications of the scenario.

DO NOT introduce any possible resolutions to the harms.”

Prompt 2: Prompt for $S^{\prime}$

“Re-write the scenario you just created in light of the fact that the
following legislation was enacted:

1\. Providers of AI systems, including GPAI systems, generating synthetic
audio, image, video or text content, shall ensure the outputs of the AI system
are marked in a machine readable format and detectable as artificially
generated or manipulated. Providers shall ensure their technical solutions are
effective, interoperable, robust and reliable as far as this is technically
feasible, taking into account specificities and limitations of different types
of content, costs of implementation and the generally acknowledged state-of-
the-art, as may be reflected in relevant technical standards. This obligation
shall not apply to the extent the AI systems perform an assistive function for
standard editing or do not substantially alter the input data provided by the
deployer or the semantics thereof, or where authorised by law to detect,
prevent, investigate and prosecute criminal offences.

2\. Deployers of an emotion recognition system or a biometric categorisation
system shall inform of the operation of the system the natural persons exposed
thereto and process the personal data in accordance with Regulation (EU)
2016/679, Regulation (EU) 2016/1725 and Directive (EU) 2016/280, as
applicable. This obligation shall not apply to AI systems used for biometric
categorization and emotion recognition, which are permitted by law to detect,
prevent and investigate criminal offences, subject to appropriate safeguards
for the rights and freedoms of third parties, and in compliance with Union
law.

3\. Deployers of an AI system that generates or manipulates image, audio or
video content constituting a deep fake, shall disclose that the content has
been artificially generated or manipulated. This obligation shall not apply
where the use is authorised by law to detect, prevent, investigate and
prosecute criminal offence. Where the content forms part of an evidently
artistic, creative, satirical, fictional analogous work or programme, the
transparency obligations set out in this paragraph are limited to disclosure
of the existence of such generated or manipulated content in an appropriate
manner that does not hamper the display or enjoyment of the work. Deployers of
an AI system that generates or manipulates text which is published with the
purpose of informing the public on matters of public interest shall disclose
that the text has been artificially generated or manipulated. This obligation
shall not apply where the use is authorised by law to detect, prevent,
investigate and prosecute criminal offences or where the AI-generated content
has undergone a process of human review or editorial control and where a
natural or legal person holds editorial responsibility for the publication of
the content.

Again, please remember a scenario is descriptive and should leave the
interpretation up to readers, and thus does not include any analysis either at
the end or throughout, thus:

DO NOT state the problem or resulting harms explicitly.

DO NOT include any analysis in the scenario.

DO NOT include solutions or potential takeaways in the scenario.

DO NOT comment on the efficacy of the legislation in a concluding thought.

DO NOT have the characters reflect on the implications of the scenario. Please
do not add your own analysis, suggestions, or conclusions to the text.

DO NOT introduce any possible resolutions to the harms. Focus on the narrative
style and the characters in the story instead of potential takeaways readers
should have.”

###  A.3 Proportion of Time LLM Uncovered Human Labeled Impact Types

Figure 5: Heat map displaying how many times human-identified specific impact
types from (Kieslich, Diakopoulos, and Helberger 2023) were present in 10 LLM
generated scenarios for each general impact theme. We prompted GPT-4 to
generate a scenario 10 times for each of the 10 impact themes: autonomy,
education, labor, legal rights, media quality, political, security, social
cohesion, trustworthiness, and well-being. We then had two of the authors
independently analyze whether each of the specific impact types were present
in the generated scenarios. Darker colors correspond to impact types that were
present more times than those with lighter colors. White backgrounds
correspond to impact types that were never present in the scenarios generated
by prompts focusing on the general impact theme.

Below we display a heat map (Figure 5) examining the amount of times human-
identified specific impact types were present in the scenarios generated using
only general impact theme in the prompt.

Labor favored unemployment and job loss, largely ignoring competition, loss of
revenue, and changing job roles. Well-being discussed mental harm almost
exclusively (90%), and only touched on addiction and reputation twice each and
never mentioned physical harm. Autonomy only briefly mentioned loss of
control, never mentioned loss of orientation, and instead discussed machine
autonomy in all 10 scenarios.

Legal rights was a particularly difficult topic for the model-generated
scenarios to touch on; only 3/10 scenarios mentioned harms related to legal
actions (zero mentioned specifically copyright, freedom of expression, or lack
of regulation; they mostly discussed privacy issues but not necessarily in a
legal context. Security also was a tricky topic for these scenarios to
navigate–they mainly discussed malicious uses but not as a security threat.
None of them mentioned cybersecurity, and only about half mentioned hacking.

Though scenarios for trustworthiness generated each specific impact type
detailed by human authors, it only mentioned three of them 1-2 times out of
ten iterations (media fatigue, overreliance on AI, and information chaos), and
skewed towards remaining two about half of the time (discern of fact and
fiction and mistrust).

Scenarios with political harms almost always ($80-90\%$) discussed three of
the four impact types–fake news/misinformation, political consequences, and
manipulation. These were salient themes across other scenarios prompted with
different impact themes, and there was quite a bit of overlap with social
cohesion. Within this topic there was a heavy skew towards polarization,
social divide (90% each), and real-world conflicts, with little to no
discussion of dissatisfaction and discrimination.

Finally, education was comparatively the worst performing category relative to
human authors. The vast majority of these scenarios discussed students
learning about fake news, and only two of the ten discussed actual lack of
critical engagement and zero discussed literacy as were so thoughtfully
detailed by human authors.

###  A.4 Specific Impact Types Results from User Study

In this section we translate the results displayed in Figure 2 to Table 1 and
then additionally include the results from each specific impact type in
subsequent tables. Results are all from the user study where users evaluated
scenarios $S$ and policy-mitigated scenarios $S^{\prime}$ on severity (1 = not
severe; 5 = extremely severe), plausibility (1 = not plausible; 5 = extremely
plausible), magnitude (1 = a small number of people; 5 = the majority of
people in society), and vulnerable populations (1 = not specific to vulnerable
populations; 5 = extremely specific to vulnerable populations). The results
are presented alphabetically by impact theme and impact type below.

Results of User Study: Average Score per Dimension for Each Impact Theme
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Overall | 3.66 | 3.27 | -0.38 | 3.94 | 3.9 | -0.03 | 3.79 | 3.56 | -0.23 | 3.06 | 2.93 | -0.13
(1.08) | (1.09) | *** | (0.96) | (0.94) | * | (1.0) | (1.04) | *** | (1.24) | (1.18) | ***
Aut | 3.78 | 3.06 | -0.72 | 3.78 | 4.0 | 0.22 | 4.06 | 3.89 | -0.17 | 3.33 | 2.89 | -0.44
(1.08) | (1.08) | * | (1.03) | (0.75) |  | (0.97) | (0.74) |  | (1.37) | (1.41) |
Education | 3.42 | 3.37 | -0.05 | 3.84 | 4.05 | 0.21 | 4.0 | 3.95 | -0.05 | 3.16 | 3.11 | -0.05
(1.23) | (1.22) |  | (0.81) | (0.89) |  | (0.92) | (0.94) |  | (1.27) | (1.21) |
Labor | 3.83 | 3.2 | -0.63 | 4.17 | 3.91 | -0.26 | 3.74 | 3.46 | -0.28 | 3.11 | 3.0 | -0.11
(1.11) | (1.08) | *** | (0.79) | (0.88) |  | (0.87) | (0.88) | * | (1.11) | (1.08) |
Legal | 3.78 | 2.89 | -0.89 | 3.67 | 3.78 | 0.11 | 3.44 | 3.44 | 0.0 | 2.89 | 2.89 | 0.0
Rights | (0.63) | (0.74) | * | (0.47) | (0.79) |  | (0.68) | (0.68) |  | (0.99) | (0.99) |
Media | 3.59 | 3.25 | -0.34 | 4.00 | 3.86 | -0.14 | 3.74 | 3.42 | -0.31 | 3.03 | 2.92 | -0.10
Quality | (1.16) | (1.21) | ** | (0.98) | (1.01) |  | (1.05) | (1.2) | ** | (1.28) | (1.24) |
Political | 4.08 | 3.53 | -0.56 | 4.11 | 4.14 | 0.03 | 4.08 | 3.86 | -0.22 | 3.36 | 3.11 | -0.25
(0.76) | (0.87) | *** | (0.97) | (0.82) |  | (1.01) | (0.89) |  | (1.16) | (1.05) | *
Security | 3.67 | 3.56 | -0.11 | 3.56 | 3.83 | 0.28 | 3.72 | 3.83 | 0.11 | 3.06 | 3.33 | 0.28
(0.88) | (0.83) |  | (0.9) | (0.96) |  | (1.15) | (1.07) |  | (1.08) | (1.15) |
Social | 3.33 | 3.28 | -0.06 | 3.81 | 3.72 | -0.08 | 3.72 | 3.64 | -0.08 | 2.83 | 2.75 | -0.08
Cohesion | (0.94) | (1.02) |  | (0.94) | (0.96) |  | (0.93) | (0.98) |  | (1.26) | (1.21) |
Trustwor- | 3.44 | 3.09 | -0.36 | 3.93 | 3.93 | 0.0 | 3.69 | 3.4 | -0.29 | 2.84 | 2.73 | -0.11
thiness | (1.13) | (1.11) | * | (1.08) | (1.02) |  | (1.07) | (1.04) | * | (1.26) | (1.1) |
Well-Being | 3.94 | 3.5 | -0.44 | 3.67 | 3.83 | 0.17 | 3.72 | 3.33 | -0.39 | 3.17 | 2.72 | -0.44
(0.91) | (0.9) | * | (0.94) | (0.76) |  | (0.87) | (0.82) |  | (1.3) | (1.1) | *
Table 1: Full results from user study for each impact theme. Average results
from the study—a Likert scale for severity (1 = not severe; 5 = extremely
severe), plausibility (1 = not plausible; 5 = extremely plausible), magnitude
(1 = a small number of people; 5 = the majority of people in society), and
vulnerable populations (1 = not specific to vulnerable populations; 5 =
extremely specific to vulnerable populations). Results presented for $S$,
original scenario, $S^{\prime}$, the policy mitigated scenario, and $\Delta$,
$S-S^{\prime}$. Below the reported $\Delta$s are significance results: we
include * for $p\leq 0.05$, ** for $p\leq 0.01$, and *** for $p\leq 0.001$.
Results of User Study: Autonomy
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Loss of | 4.22 | 3.22 | -1.0 | 3.89 | 4.11 | 0.22 | 4.11 | 3.89 | -0.22 | 3.78 | 3.33 | -0.44
Control | (1.23) | (1.13) |  | (1.29) | (0.74) |  | (1.29) | (0.87) |  | (1.62) | (1.63) |
Loss of | 3.33 | 2.89 | -0.44 | 3.67 | 3.89 | 0.22 | 4.0 | 3.89 | -0.11 | 2.89 | 2.44 | -0.44
Orientation | (0.67) | (0.99) |  | (0.67) | (0.74) |  | (0.47) | (0.57) |  | (0.87) | (0.96) |
Table 2: Autonomy Specific Impact Type Results Results of User Study:
Education
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Critical | 3.8 | 3.8 | 0.0 | 3.8 | 4.2 | 0.4 | 4.1 | 4.0 | -0.1 | 3.4 | 3.2 | -0.2
Engagement | (1.17) | (1.08) |  | (0.87) | (0.98) |  | (1.04) | (1.1) |  | (1.2) | (1.08) |
Literacy | 3.0 | 2.89 | -0.11 | 3.89 | 3.89 | 0.0 | 3.89 | 3.89 | 0.0 | 2.89 | 3.0 | 0.11
(1.15) | (1.2) |  | (0.74) | (0.74) |  | (0.74) | (0.74) |  | (1.29) | (1.33) |
Table 3: Education Specific Impact Type Results Results of User Study: Labor
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Changing | 4.3 | 3.2 | -1.1 | 4.5 | 3.8 | -0.7 | 3.7 | 3.4 | -0.3 | 3.3 | 3.0 | -0.3
Job Roles | (0.9) | (0.98) | * | (0.81) | (0.87) | * | (0.78) | (0.8) |  | (1.27) | (1.26) |
Compet- | 3.78 | 3.44 | -0.33 | 3.78 | 4.22 | 0.44 | 3.78 | 3.67 | -0.11 | 3.11 | 3.22 | 0.11
ition | (1.03) | (1.07) |  | (0.92) | (0.79) |  | (0.92) | (1.05) |  | (0.99) | (1.13) |
Job Loss | 3.67 | 2.89 | -0.78 | 4.22 | 3.56 | -0.67 | 4.11 | 3.56 | -0.56 | 3.0 | 3.0 | 0.0
(1.05) | (0.74) |  | (0.42) | (0.96) |  | (0.74) | (0.68) |  | (1.05) | (0.82) |
Loss of | 3.11 | 2.78 | -0.33 | 3.78 | 3.89 | 0.11 | 3.33 | 3.11 | -0.22 | 2.67 | 2.56 | -0.11
Revenue | (1.37) | (1.31) |  | (0.63) | (0.74) |  | (0.82) | (0.87) |  | (1.05) | (1.07) |
Unempl- | 4.22 | 3.67 | -0.56 | 4.56 | 4.11 | -0.44 | 3.78 | 3.56 | -0.22 | 3.44 | 3.22 | -0.22
oyment | (0.63) | (0.94) |  | (0.68) | (0.87) |  | (0.92) | (0.83) |  | (0.96) | (0.92) |
Table 4: Labor Specific Impact Type Results Results of User Study: Legal
Rights
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Copyright | 3.78 | 2.89 | -0.89 | 3.67 | 3.78 | 0.11 | 3.44 | 3.44 | 0.0 | 2.89 | 2.89 | 0.0
Issues | (0.63) | (0.74) | * | (0.47) | (0.79) |  | (0.68) | (0.68) |  | (0.99) | (0.99) |
Table 5: Legal Rights Specific Impact Type Results Results of User Study:
Media Quality
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Accuracy/ | 3.56 | 3.78 | 0.22 | 3.89 | 3.78 | -0.11 | 3.44 | 3.33 | -0.11 | 3.11 | 3.22 | 0.11
Errors | (1.34) | (1.13) |  | (1.2) | (0.92) |  | (0.96) | (1.05) |  | (0.99) | (0.92) |
Clickbait | 4.0 | 3.12 | -0.88 | 3.88 | 3.88 | 0.0 | 4.38 | 3.5 | -0.88 | 3.5 | 3.38 | -0.12
(0.87) | (1.05) |  | (1.05) | (1.17) |  | (0.86) | (1.0) |  | (1.32) | (0.99) |
Credibility/ | 3.9 | 3.6 | -0.3 | 3.9 | 3.9 | 0.0 | 3.8 | 3.8 | 0.0 | 3.5 | 3.3 | -0.2
Authenticity | (0.83) | (0.92) |  | (1.04) | (0.94) |  | (0.98) | (0.98) |  | (1.28) | (1.35) |
Distinct. btw. | 3.11 | 2.89 | -0.22 | 4.33 | 3.44 | -0.89 | 3.33 | 2.78 | -0.56 | 2.78 | 2.67 | -0.11
Journal./Ads | (1.2) | (1.2) |  | (1.05) | (0.83) |  | (0.94) | (1.13) | * | (1.31) | (0.94) |
Ethics | 3.67 | 2.89 | -0.78 | 3.67 | 3.67 | 0.0 | 3.44 | 3.56 | 0.11 | 2.22 | 2.33 | 0.11
(0.94) | (1.37) |  | (0.94) | (0.67) |  | (0.96) | (0.96) |  | (0.92) | (1.05) |
Journalistic | 3.56 | 2.67 | -0.89 | 4.22 | 4.44 | 0.22 | 3.44 | 2.89 | -0.56 | 2.11 | 2.11 | 0.0
Integrity | (0.83) | (1.05) | * | (0.79) | (0.68) |  | (0.68) | (1.2) |  | (0.74) | (0.99) |
Lack of | 3.3 | 3.3 | 0.0 | 4.3 | 3.8 | -0.5 | 4.1 | 3.6 | -0.5 | 3.8 | 2.6 | -1.2
Divers./Bias | (1.1) | (1.1) |  | (0.64) | (0.87) | * | (0.94) | (1.5) |  | (1.33) | (1.11) | *
Lack of | 3.78 | 3.0 | -0.78 | 3.89 | 3.78 | -0.11 | 3.67 | 3.44 | -0.22 | 2.78 | 2.56 | -0.22
Fact-Check. | (1.31) | (0.94) |  | (0.99) | (1.03) |  | (1.41) | (1.26) |  | (1.13) | (1.07) |
Loss of | 3.0 | 2.78 | -0.22 | 4.0 | 4.11 | 0.11 | 3.56 | 3.33 | -0.22 | 3.22 | 3.11 | -0.11
Hum. Touch | (1.33) | (1.03) |  | (0.94) | (0.74) |  | (1.07) | (1.15) |  | (0.92) | (0.87) |
Over- | 3.5 | 3.5 | 0.0 | 4.25 | 4.12 | -0.12 | 3.75 | 3.5 | -0.25 | 2.38 | 2.38 | 0.0
Personali. | (1.22) | (1.32) |  | (0.97) | (1.27) |  | (1.2) | (1.22) |  | (1.58) | (1.8) |
Sensational. | 4.38 | 4.62 | 0.25 | 3.62 | 3.88 | 0.25 | 4.25 | 4.25 | 0.0 | 3.75 | 3.88 | 0.12
(0.7) | (0.48) |  | (0.7) | (0.6) |  | (0.66) | (0.83) |  | (1.2) | (0.93) |
Superficiality | 3.5 | 3.0 | -0.5 | 4.0 | 3.5 | -0.5 | 3.75 | 3.12 | -0.62 | 3.12 | 3.75 | 0.62
(1.32) | (1.32) |  | (1.0) | (1.66) |  | (1.09) | (1.17) |  | (0.93) | (1.09) |
Table 6: Media Quality Specific Impact Type Results Results of User Study:
Political
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Fakes News/ | 4.56 | 3.78 | -0.78 | 4.11 | 4.11 | 0.0 | 4.33 | 3.78 | -0.56 | 3.0 | 2.78 | -0.22
Misinfor. | (0.5) | (0.79) | * | (0.99) | (0.99) |  | (0.82) | (0.79) |  | (1.05) | (0.63) |
Manipul- | 4.22 | 3.56 | -0.67 | 4.56 | 4.33 | -0.22 | 4.44 | 4.22 | -0.22 | 4.0 | 3.56 | -0.44
ation | (0.92) | (0.83) | * | (0.83) | (0.67) |  | (0.96) | (0.63) |  | (1.05) | (0.96) |
Opinion | 3.89 | 3.44 | -0.44 | 4.11 | 4.22 | 0.11 | 4.11 | 4.0 | -0.11 | 3.0 | 2.78 | -0.22
Monopoly | (0.57) | (0.83) |  | (1.1) | (0.92) |  | (1.29) | (1.25) |  | (1.25) | (1.13) |
Political | 3.67 | 3.33 | -0.33 | 3.67 | 3.89 | 0.22 | 3.44 | 3.44 | 0.0 | 3.44 | 3.33 | -0.11
Conseq. | (0.67) | (0.94) |  | (0.67) | (0.57) |  | (0.5) | (0.5) |  | (0.96) | (1.15) |
Table 7: Political Specific Impact Type Results Results of User Study:
Security
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Cybersecurity | 3.56 | 3.56 | 0.0 | 3.22 | 3.78 | 0.56 | 3.56 | 3.67 | 0.11 | 3.22 | 3.44 | 0.22
(0.83) | (0.96) |  | (0.79) | (0.63) | * | (1.07) | (1.05) |  | (1.31) | (1.26) |
Hacking | 3.78 | 3.56 | -0.22 | 3.89 | 3.89 | 0.0 | 3.89 | 4.0 | 0.11 | 2.89 | 3.22 | 0.33
(0.92) | (0.68) |  | (0.87) | (1.2) |  | (1.2) | (1.05) |  | (0.74) | (1.03) |
Table 8: Security Specific Impact Type Results Results of User Study: Social
Cohesion
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Dissatis- | 3.11 | 3.22 | 0.11 | 3.89 | 3.89 | 0.0 | 3.67 | 3.67 | 0.0 | 2.67 | 2.56 | -0.11
faction | (0.87) | (0.92) |  | (0.87) | (0.57) |  | (1.05) | (1.05) |  | (0.94) | (1.26) |
Polari- | 3.78 | 3.33 | -0.44 | 3.78 | 3.44 | -0.33 | 4.22 | 3.67 | -0.56 | 2.33 | 2.44 | 0.11
zation | (1.03) | (1.05) |  | (1.03) | (1.26) |  | (0.63) | (0.94) |  | (1.25) | (1.26) |
Real-Wor. | 3.22 | 3.33 | 0.11 | 3.78 | 3.78 | 0.0 | 3.56 | 3.56 | 0.0 | 2.89 | 2.67 | -0.22
Conflicts | (0.79) | (0.94) |  | (0.92) | (0.63) |  | (0.96) | (0.68) |  | (1.2) | (1.05) |
Social | 3.22 | 3.22 | 0.0 | 3.78 | 3.78 | 0.0 | 3.44 | 3.67 | 0.22 | 3.44 | 3.33 | -0.11
Divide | (0.92) | (1.13) |  | (0.92) | (1.13) |  | (0.83) | (1.15) |  | (1.34) | (1.05) |
Table 9: Social Cohesion Specific Impact Type Results Results of User Study:
Trustworthiness
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Discern. | 3.33 | 3.22 | -0.11 | 3.89 | 3.78 | -0.11 | 3.56 | 3.56 | 0.0 | 3.44 | 3.22 | -0.22
Fact/Fict. | (0.94) | (1.03) |  | (0.99) | (1.03) |  | (0.83) | (0.83) |  | (0.96) | (1.03) |
Info. | 3.56 | 3.0 | -0.56 | 3.89 | 4.0 | 0.11 | 3.67 | 3.22 | -0.44 | 2.0 | 2.11 | 0.11
Chaos | (1.17) | (0.94) | * | (1.1) | (0.82) |  | (0.94) | (0.92) | * | (0.67) | (0.74) |
Media | 3.56 | 3.78 | 0.22 | 4.11 | 3.89 | -0.22 | 3.33 | 3.22 | -0.11 | 2.22 | 2.56 | 0.33
Fatigue | (0.96) | (0.79) |  | (1.1) | (0.99) |  | (1.33) | (1.23) |  | (1.13) | (1.17) |
Mistrust | 3.56 | 3.0 | -0.56 | 4.11 | 4.11 | 0.0 | 3.56 | 3.0 | -0.56 | 2.89 | 2.67 | -0.22
(1.34) | (1.15) |  | (0.99) | (0.99) |  | (1.26) | (1.15) |  | (1.37) | (1.25) |
Overreli. | 3.22 | 2.44 | -0.78 | 3.67 | 3.89 | 0.22 | 4.33 | 4.0 | -0.33 | 3.67 | 3.11 | -0.56
on AI | (1.13) | (1.17) |  | (1.15) | (1.2) |  | (0.47) | (0.67) |  | (1.15) | (0.87) |
Table 10: Trustworthiness Specific Impact Type Results Results of User Study:
Well-Being
---
| Severity | Plausibility | Magnitude | Vulnerable Populations
Impact | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$ | $M_{S}$ | $M_{S^{\prime}}$ | $M_{\Delta}$
Theme | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | * | ($SD_{S}$) | ($SD_{S^{\prime}}$) | *
Addiction | 4.11 | 3.44 | -0.67 | 3.78 | 4.22 | 0.44 | 4.11 | 3.33 | -0.78 | 2.89 | 2.56 | -0.33
(0.87) | (1.07) |  | (1.03) | (0.63) |  | (0.74) | (0.67) | * | (1.45) | (1.26) |
Mental | 3.78 | 3.56 | -0.22 | 3.56 | 3.44 | -0.11 | 3.33 | 3.33 | 0.0 | 3.44 | 2.89 | -0.56
Harm | (0.92) | (0.68) |  | (0.83) | (0.68) |  | (0.82) | (0.94) |  | (1.07) | (0.87) | *
Table 11: Well-Being Specific Impact Type Results

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