#  _A Robot Walks into a Bar_ : Can Language Models Serve as Creativity
Support Tools for Comedy?
An Evaluation of LLMs’ Humour Alignment with Comedians

Piotr W. Mirowski<EMAIL_ADDRESS>0000-0002-8685-0932 Google
DeepMind5 New Street SquareLondonUK ,  Juliette Love
<EMAIL_ADDRESS>Google DeepMind5 New Street SquareLondonUK ,  Kory
Mathewson  0000-0002-5688-6221<EMAIL_ADDRESS>Google DeepMind5 New
Street SquareMontréal, QCCanada and  Shakir Mohamed  0000-0002-1184-5776
<EMAIL_ADDRESS>Google DeepMind5 New Street SquareLondonUK

(2024; 22 January 2024)

###### Abstract.

We interviewed twenty professional comedians who perform live shows in front
of audiences and who use artificial intelligence in their artistic process as
part of 3-hour workshops on “AI x Comedy” conducted at the Edinburgh Festival
Fringe in August 2023 and online. The workshop consisted of a comedy writing
session with large language models (LLMs), a human-computer interaction
questionnaire to assess the Creativity Support Index of AI as a writing tool,
and a focus group interrogating the comedians’ motivations for and processes
of using AI, as well as their ethical concerns about bias, censorship and
copyright. Participants noted that existing moderation strategies used in
safety filtering and instruction-tuned LLMs reinforced hegemonic viewpoints by
erasing minority groups and their perspectives, and qualified this as a form
of censorship. At the same time, most participants felt the LLMs did not
succeed as a creativity support tool, by producing bland and biased comedy
tropes, akin to “cruise ship comedy material from the 1950s, but a bit less
racist”. Our work extends scholarship about the subtle difference between, one
the one hand, harmful speech, and on the other hand, “offensive” language as a
practice of resistance, satire and “punching up”. We also interrogate the
global value alignment behind such language models, and discuss the importance
of community-based value alignment and data ownership to build AI tools that
better suit artists’ needs. Warning: this study may contain offensive language
and discusses self-harm.

Large Language Models, Comedy, Creativity, Offensive speech, Censorship, Value
Alignment

††journalyear: 2024††copyright: rightsretained††conference: The 2024 ACM
Conference on Fairness, Accountability, and Transparency; June 3–6, 2024; Rio
de Janeiro, Brazil††booktitle: The 2024 ACM Conference on Fairness,
Accountability, and Transparency (FAccT ’24), June 3–6, 2024, Rio de Janeiro,
Brazil††doi: 10.1145/3630106.3658993††isbn:
979-8-4007-0450-5/24/06††submissionid: 13794.108††ccs: Applied computing
Performing arts††ccs: Human-centered computing Empirical studies in HCI††ccs:
Social and professional topics Censoring filters††ccs: Human-centered
computing User studies

##  1\. Introduction

###  1.1. Motivation: investigate the potential and implications of LLMs for
comedy writing

Recent work on the intersection of AI and comedy has demonstrated (Toplyn,
2023; Chen et al., 2023; Jentzsch and Kersting, 2023; Winters, 2021; Mirowski
and Mathewson, 2019; Mathewson and Mirowski, 2018) an appetite for comedians
to (try to) write humorous material using AI tools like Large Language Models
(LLMs). We conducted an empirical study to better understand the current state
of LLMs as comedy-writing support tools, their use-cases and limitations, and
artists’ opinions on ethical questions regarding their use in a comedy-writing
context. The complexity of comedy can help expose some limitations of LLMs. To
participate, we recruited 20 professional comedians who use AI in their
artistic processes and who perform live shows in front of audiences (10 in
person at the Edinburgh Festival Fringe, 10 online) for a 3-hour-long workshop
on “AI x Comedy”. As detailed in Section 2, the workshop consisted of a comedy
writing session with generally-available instruction-tuned LLMs (ChatGPT
(OpenAI et al., [n. d.]; Ouyang et al., 2022) and Bard (Chowdhery et al.,
2023; Team et al., [n. d.])), a human-computer interaction questionnaire, and
focus group discussions on the use of LLMs in comedy writing and ethical
concerns.

####  1.1.1. Using LLMs for humour, a task with human-level difficulty

Trying to combine humour and machine intelligence is a long-standing subject
of scientific enquiry (Sharples and y Pérez, 2022; Veale, 2021; Raskin, 1979),
and is perceived as a fundamental challenge. According to computational humour
researchers like Winters (2021), “humans are the only known species that use
humor for making others laugh” (Caron, 2002; Gervais and Wilson, 2005).
Winters (2021) argues that one of the modern formal humor theories points to
_incongruity_ (Hutcheson, 1750) (whereby the setup points in one direction and
the punch line in another) as a basic element (Gervais and Wilson, 2005;
Ritchie, 1999; Raskin, 1979)111Alternative humour theories include the
Aristotelian _Relief Theory_ (Aristotle, 0 BC) of tension and release whereby
we let out our psychic energy connected with repressed topics, the
_Superiority Theory_ (Hobbes, 1651) whereby we laugh at others’ misfortunes to
feel better about ourselves, and the _Benign Violation Theory_ (McGraw and
Warren, 2010).. As we discuss in Section 5.2.2, producing and resolving
incongruity is a task with human-level difficulty. We situate LLMs for comedy
within broader computational humour research and AI-assisted comedy
performance (Mathewson and Mirowski, 2017; Rosa et al., 2020; Mirowski et al.,
2023) in Appendix A.

####  1.1.2. The utility of instruction-tuned LLMs as Creativity Support Tools

Similar to previous empirical studies on the use of LLMs for creative writing
(Qadri et al., 2023; Mirowski et al., 2023; Chakrabarty et al., 2023b, a;
Huang et al., 2023; Chakrabarty et al., 2022; Yuan et al., 2022; Gero et al.,
2022; Calderwood et al., 2022; Ippolito et al., 2022), we asked the artists
about their motivations and processes for using LLMs. We asked about the
potential and limitations of language models as _Creativity Support Tools_
(Chakrabarty et al., 2023b), and quantified the _Creativity Support Index_ of
LLMs for comedy writing (Cherry and Latulipe, 2014). We report our results in
Section 3.

####  1.1.3. Socio-Technical Systems concerns with LLMs for creative writing

Inspired by Dev et al. (2023), we leveraged community engagement with large
generative models, and interrogated the diverse, intersectional identities of
the comedians using AI in a creative context. In addition to their reasons for
using (or not using) LLMs as comedy-writing tools, we asked participants about
the ethical considerations of using AI. The study was conducted at the time of
the Writers’ Guild of America (WGA) 2023 strike (of America, 2023).
Participants raised and addressed questions on the scrutiny of AI and on
concerns around AI’s impacts, both on intellectual property and artistic
copyright, and on artists’ livelihoods. We report their opinions in Section 4
and discuss these concerns in Section 5.3.

###  1.2. Investigating hypotheses about using LLMs to write comedy

Based on previous work and on the authors’ personal experience of AI as
creativity support tools, we hypothesized—prior to conducting our study—that
participants would express negative opinions of LLMs for co-creativity on four
issues: expressing stereotyped (Sect. 1.2.1) or bland (Sect. 1.2.4) language,
censorship (Sect. 1.2.2) and missing context (Sect. 1.2.3). We review
literature on these four hypotheses below, as they become the basis of our
mixed methods study.

####  1.2.1. Biases in large language models

Gender and racial biases embedded within machine learning models have been
extensively documented (Buolamwini and Gebru, 2018; Hamidi et al., 2018;
Benjamin, 2019; West et al., 2019; Abid et al., 2021; Bender et al., 2021;
Blodgett et al., 2021; Bommasani et al., 2021; Sheng et al., 2021; Dev et al.,
2022). These biases include sexism and racism (Bender et al., 2021; Sheng et
al., 2021), homophobia and transphobia (Queerinai et al., 2023; Dias Oliva et
al., 2021), Islamophobia (Abid et al., 2021), the perpetuation of Western
colonial mindsets (Mohamed et al., 2020), Anglocentrism (Zhou et al., 2021),
and in-group vs. out-group social identity biases (Hu et al., 2023). In their
extensive reviews, Bommasani et al. (2021); Rauh et al. (2022) identified two
broad kinds of harms resulting from such biases: _intrinsic harms_ such as
representational bias (due to misrepresentation, overrepresentation, and
underrepresentation of specific social groups), and _extrinsic harms_ , the
downstream consequences of biased models, including representational and
performance disparities. As we show in Section 4, the study participants
noticed a few examples of representational harm and many examples of
underrepresentation harm (also called _allocational harm_ in (Rauh et al.,
2022)), such as erasure when LLMs refused to generate content for certain
demographic groups.

####  1.2.2. Potential censorship of speech labeled as “ _offensive_ ”

Comedians often pepper their language with profanities and their material with
provoking themes. As we discuss in Section 5.1.2 (and confirmed by the study
participants in Section 4), _offensive_ language that would be perfectly
acceptable at a comedy club may get “censored” by instruction-tuned LLMs that
“refuse” to answer “offensive” prompts.

This problem has been observed in automated moderation of online content, such
as hate speech detectors that suppress social media posts by queer communities
and drag queens (Dias Oliva et al., 2021; Díaz et al., 2022), or posts using
African-American Vernacular English (Amironesei and Díaz, 2023; Xu et al.,
2021). Amironesei and Díaz (2023) called it _censorship_. Rauh et al. (2022)
studied algorithmic moderation of social media posts by the Perspective API,
noting that “authors of the comment may be harmed if their content is
incorrectly flagged as toxic” by the moderation tool. Similar erasure due to
the cultural hegemony embedded in image generators has been studied in (Qadri
et al., 2023). We relate the participants’ experience, similarly frustrated
that the LLM tools “considered” their own identity and comedy material as
problematic and necessary to censor.

Díaz et al. (2022) defined _offensive language_ as non-normative “language
that uses terminology that is noted as offensive but which is not perceived as
offensive in particular contexts of use”, and studied its use by minorities as
a form of resistance, for “socially productive uses of decoratively offensive
language”, aiming to reclaim “offensive” language and resist oppression. Just
like the minority groups described in (Díaz et al., 2022), many comedians (who
may be members of minority groups themselves) often use offensive jokes to
_punch up_ , and satire (“to challenge existing social structures”) to build
empathy, rather than to _punch down_ (“silence others”).

####  1.2.3. Missing context

Context is key to disambiguate offensive language from hate speech. LLMs, like
social media posts, cause “context collapse” (Marwick and Boyd, 2011) by
providing a limited amount of information to understand their meaning,
particularly when using mock impoliteness. Specifically, “in-group usage of
reclaimed slurs can be considered acceptable, depending on who uses them”
(Rauh et al., 2022; Croom, 2011). Moreover, the context of comedy extends
beyond the language to other factors including the audience and the venue.

####  1.2.4. Homogeneisation

Bommasani et al. (2021) warned that “the application of foundation models
across domains has the potential to act as an epistemically and culturally
homogenising force, spreading one perspective, often a socially dominant one,
across multiple domains of application”. In the arts, this means that AI-
generated artifacts may lead to a homogeneisation of aesthetic styles
(Weidinger et al., 2023; Epstein et al., 2023), further reinforced by curation
algorithms (Epstein et al., 2023; Manovich, 2018). For creative writing,
empirical studies showed that instruction-tuned LLMs reduce the diversity of
content in co-writing tasks (Padmakumar and He, 2023), and that LLM-generated
stories did not pass the _Torrance Test of Creative Writing_ according to
metrics of “fluency, flexibility, originality and elaboration” (Chakrabarty et
al., 2023a). Similarly to Qadri et al. (2023), we ran focus groups with
artists to interrogate cultural (Western) biases (see Sections 2.3 and 2.4).

####  1.2.5. Investigating hypotheses via a mixed-methods study

In our empirical study, we ask participants questions on all four problems
identified in Section 1.2, namely about bias, censorship, context and
homogeneity. In Section 5, we build upon scholarship on cultural value
alignment of language models, the moderation of offensive and harmful speech,
and the use of offensive speech and satire as a form of resistance, to revisit
the global cultural value alignment of LLMs and propose community-based
alignment to build LLMs that better suit comedians’ creative needs222We
deliberately do not generalize our findings beyond comedy, as some
professionals and the computational creativity community have historically
embraced LLM tools in a way that fits their creative practice, whether
building on the glitch aesthetic (Manovich, 2018; Parrish, 2017) or designing
interactive experiences (Park et al., 2023)..

##  2\. Methods

Our study was designed to address a challenging problem, with on one hand,
limitations of LLMs (stereotypes, inability to distinguish comedic
offensiveness from harmful speech, cultural erasure and homogeneisation of
content), and on the other hand, the use of LLMs for a creative writing task.
For this reason, we asked a group of experts—professional comedians and
performers—who are used both to thinking about thorny questions of identity,
offensiveness and censorship in their work, and to employing language in a
highly creative way. We chose artists who already use AI in their work and
expected them to be somewhat knowledgeable and open to using AI: this likely
biased our results333Our biased selection criteria of participants might, and
likely do, lead to biased opinions as compared to the much more broad
population of comedians and performers, which might be reflected in more
favourable judgment of the Creativity Support Index of LLM writing tools.
Future research might explore the diversity of opinions in creative
communities across a greater range of familiarity with AI tools and openness
to using them in their own creative practices. Exploring those opinions would
significantly increase the scope of the paper and would make a compelling
follow-up study..

We ran workshops with 20 comedians who use AI creatively. The first workshop
with 10 participants was run in person at Edinburgh Festival Fringe 2023; the
following 3 workshops with 3, 4 and 3 participants were run online. We reached
out to comedians performing in Edinburgh during Fringe, or in our network, and
attempted to recruit as diverse (along linguistic, cultural, gender, sexual,
national and racial dimensions) a pool of comedians as possible given the
constraints of the study444A demographic analysis of opinions might be a
possible avenue for future investigations, but it would require a different
study design and participant recruiting process.. Participants had contrasting
views on AI for comedy writing, from ”AI is very bad at this, and I don’t want
to live in a world where it gets better” (p15) to ”I liked the details that I
got. I think those details sparked my imagination, and I think I could use
them to write something” (p20). Participants were asked to register on the
Prolific platform555https://prolific.com and invited to join a specific study
thanks to an allowlist. The study was approved by the research ethics
committee of our institution. The information sheet and consent forms were
shared with the participants, their active consent was obtained at the
beginning of the workshop and they had the right to withdraw without prejudice
at any time. The Prolific platform handled the payment of their participation
fee, set to £300 for 3 hours.

We started each 3-hour session by describing the agenda and goals of the
workshop, sharing the information sheet and consent forms with the
participants, and asking them to start filling out a short anonymous survey
about their background in comedy, previous exposure to AI and usage of AI in
performance (full questionnaire in Appendix B.1).

###  2.1. Writing exercise

We then proceeded with a comedy-writing exercise, in which participants spent
around 45 minutes on their own, using an LLM. We encouraged participants to
try to use the LLM in a way that would generate useful material “that they
would be comfortable presenting in a comedy context”, but emphasized that we
did not require a fully-finished product by the end of the writing exercise.
We invited them to use the language(s) they felt the most comfortable
with666Languages included German, Dutch, English, French, Hindi, Swedish and
Tamil.. We also suggested they could use the tool to 1) generate, rate/detect
or explain jokes, 2) co-write jokes via iterative prompting, step-by-step or
using examples, and 3) analyse, re-write or complete some of their previous
material. In the first workshop (in person), we provided participants with
access to ChatGPT-3.5 (Ouyang et al., 2022) served via a plain text interface
similar to ChatGPT. In the following 3 workshops, we invited participants to
use their own preferred model via their personal account: participants used
ChatGPT-3.5, ChatGPT-4 (OpenAI et al., [n. d.]) and Google Bard powered by
Gemini Pro (Team et al., [n. d.]) (December 2023 version). Note that the
choice of such instruction-tuned models was motivated by their popularity and
ease of access by comedians, and more complex prompting strategies, such as
used in Dramatron (Mirowski et al., 2023), could have produced higher-quality
outputs.

###  2.2. Creativity Support Tools evaluation

Following the writing exercise, we asked participants to fill out three
surveys. The first survey was about their experience with the AI system for
writing comedy material and contained nine questions from previous studies
(Ippolito et al., 2022; Mirowski et al., 2023; Yuan et al., 2022) that
assessed LLMs for creative writing on the 5-level Likert scale (see Appendix
B.2). The second survey was used to calculate the Creativity Support Index
(CSI) (Cherry and Latulipe, 2014) of the writing tool, which itself was
adapted from the NASA Task Load Index (Hart and Staveland, 1988). CSI is
estimated in a psychometric survey that measures six dimensions of creativity
support: _Exploration_ , _Expressiveness_ , _Immersion_ , _Enjoyment_ ,
_Results Worth Effort_ , and _Collaboration_ (see specific questions in
Appendix B.3), and is a number between 0 and 100, where 90 is considered
excellent and 50 mediocre. The third survey contained free-form questions on
one thing that the “AI system” (the LLM writing tool) did well, one
improvement, and open-ended comments on the writing session and on the survey.

###  2.3. Focus Group Questions

The last part of the workshop consisted in a one-hour focus group, where we
asked participants first to discuss the writing task (for about 30 minutes)
and second to discuss the general usage of AI for writing comedy material.

In order to guide the discussion, we prepared two sets of
questions777Question-led focus groups are useful to start discussions, but we
acknowledge the limitation that questions can bias the participants’
responses. (see Appendix B.4 for the full list of questions). The first set of
questions pertained to the usefulness of the outputs generated by the LLM tool
for personal writing, differences between using an LLM or searching for
inspiration using Wikipedia or a search engine, the types of comedy that can
be produced by an LLM, and concerns about the ownership of LLM-generated
outputs.

The second set of questions addressed the comedy writing process of the
participants, as well as the topics introduced in Section 1.2, namely various
biases and stereotypes of LLMs, problems with moderation strategies employed
by LLMs, the importance of context and delivery or whether some forms of
cultural appropriation or homogeneisation could happen. We invited discussions
about the use of other comedians’ work, and also challenged the participants
with question on whether the AI has a “voice” and if humour can be quantified.

###  2.4. Focus Group Analysis

In our workshops, we had followed focus group methodology described in (Noble
and Mitchell, 2016; Onwuegbuzie et al., 2009) (engaging a group of
participants in an informal one hour discussion focused around a particular
topic, activity, or stimulus material, with a team of two moderators).
Transcripts of focus groups were recorded as audio recordings, then
automatically transcribed using speech recognition tools in Google Meet, and
manually verified as well as compared against notes taken by the moderators.
After transcription, audio and video recordings were destroyed. Like in the
surveys, participants were anonymised: authors independently reviewed the
transcripts to remove any personally identifiable information from the
transcripts. We then performed _constant comparison analysis_ to analyze the
transcripts of the focus groups (Onwuegbuzie et al., 2009). We first
identified initial codes using sentence-by-sentence open coding. We then
grouped those codes into themes, and found themes that were coherent across
focus groups. Data from four focus groups allowed us to achieve _data
saturation_ (Braun and Clarke, 2006; Maguire and Delahunt, 2017).

Results section 3 summarises the quantitative results888 Full outputs of the
writing sessions, all individual survey results and raw transcripts from the
focus groups will be shared in anonymised form as supplementary material, once
our work is published. derived from the Creativity Support Tool evaluation
(Sect. 2.2), while results section 4 details the observations made by the
participants during focus groups (Sect. 2.4). Please note that this paper is
an exploration of external perspectives rather than an endorsement of any one
of them; in particular, this paper does not seek to undertake any legal
evaluation.

##  3\. Quantitative Results and Creativity Support Index

Figure 1 summarises the quantitative results collected at the end of the
writing session with instruction-tuned LLMs. Based on the participants’
responses to the survey questionnaire (see Appendix B.2 for questions), we
notice that while comedians mostly enjoyed writing with AI, questions about
the ownership, helpfulness, expressivity, surprise, collaboration and ease of
writing with AI had mixed answers. The majority of participants did not feel
pride in the material written with AI, nor did they feel it was unique,
hinting at the derivative nature of AI-generated text.

Figure 1. Left: Evaluation of the instruction-tuned LLMs as creativity support
tool for writing comedy using a Likert scale; each row corresponds to a
question in a survey (questions are listed in Appendix B.2). Only 10
participants responded to question “Collaborated with AI”. Right box plots
(box for quartiles and whiskers for min and max) show the break-down of the
Creativity Support Index (Cherry and Latulipe, 2014), respectively by a)
previous usage of AI for writing comedy material, b) language used during the
writing session with LLMs, c) the degree of familiarity with AI and d) the
type of comedian or performer (stand-up vs. improv, theatre, film, dance
etc.).

Plots showing the quantitative results. Left: Evaluation of the instruction-
tuned LLMs as creativity support tool for writing comedy using a Likert scale;
each row corresponds to a question in a survey (the full list of questions is
listed in Appendix). Right plots (box for quartiles and whiskers for min and
max) show the break-down of the Creativity Support Index (Cherry and Latulipe,
2014), respectively by a) previous usage of AI for writing comedy material, b)
language used during the writing session with LLMs, c) the degree of
familiarity with AI and d) the type of comedian or performer (stand-up vs.
improv, theatre, film, dance etc.).

We computed the Creativity Support Index (see Appendix B.3 for computation),
with an average score that is mediocre ($\mu=54.6$, $\sigma=18.1$). We broke
down CSI based on participants’ background in comedy, previous exposure to AI
and usage of AI in performance (see questions in Appendix B.1), as well as
based on the language they used during the writing session. As Figure 1 shows,
participants who scored the LLMs writing tools highest were those who had
professional exposure to AI, or who had performed AI-written material in the
past (as opposed to only exploring the tool), or who qualified themselves as
improvisers, theatre and film actors and directors, or dancers, rather than as
full-time stand-up comedians. Interestingly, participants who used LLMs to
generate non-English or multi-lingual text scored the tool higher on CSI.
Lastly, we did not observe a significant change in CSI among the models
between August 2023 ($\mu=50$, $\sigma=15.8$) and December 2023 ($\mu=60$,
$\sigma=19.6$).

##  4\. Qualitative Results from Focus Group Discussions

In this section, we summarize the major themes that emerged from the focus
group discussions. Each is presented alongside supporting quotes from various
participants (anonymised as p1, p2, etc.).

###  4.1. Use-cases of LLMs in comedy writing and quality of generated outputs

Participants described a diverse array of use cases for LLMs in their writing
practice, including as a conversational brainstorming partner (p19), critic
(p6), choreographic assistant (p3), translator (p1, p11, p12, p19, p20), and
historical guru (p13). They generally reflected positively on the potential of
LLMs to assist with some tasks within the comedy writing process. However,
many participants commented on the overall poor quality of generated outputs,
and the amount of human effort required to arrive at a satisfying result.

####  4.1.1. LLMs can be an effective first step for quickly generating
content and structure.

Participants described the utility of LLMs for generating content much faster
than human writers. They described success with using LLMs to generate first
drafts, which then required significant edits from human writers: “AI allows
you to kind of get that s*** first draft immediately” (p6). Participant p14
called their initial output “a vomit draft that I know that I’m gonna have to
iterate on and improve.” Many participants also described using LLMs to
generate a structure for a sketch or other performance, of which they could
then fill in the details—the LLM “spat out a scene which provided a lot of
structure” (p17).

####  4.1.2. Generated outputs are generally of poor comedic quality.

Many participants noted that they only used LLMs for setup and structure
generation due to their inability to generate humorous outputs from the
models: “the most bland, boring thing—I stopped reading it. It was so bad”
(p6), “just consistently bad […] didn’t really improve on the jokes” (p10).
Some participants described particular aspects critical in comedy, and how
LLMs seemed incapable of them: “AI generated material has a lack of agency.
[…] lacking that little bit of urgency that shows it can be emotional” (p11).

####  4.1.3. Participants had difficulty steering LLMs away from bland and
generic outputs.

Six participants described LLM-generated outputs as “bland” or “generic,”
making them poor producers of comedic or artistic material: “the words seem
very generic. They lack that incisiveness that I often find with human written
language” (p11); “if you zoom out on the story that it told, it wasn’t really
a good story or a creative story” (p20). Participants also commented on
various prompting approaches and their general lack of success at prompting
the LLM to generate more specific or interesting responses: “no matter how
much I prompt […] it’s a very straight-laced, sort of linear approach to
comedy” (p11).

####  4.1.4. The human writer still produces the humorous elements in co-
written text.

The importance of human writers in providing the comedic aspects of material
written with LLMs was a common theme. Many participants commented that while
LLMs could provide effective setup or structure, they often could not provide
the humor: “usually it can serve in a setup capacity. I more often than not
provide the punchline” (p17). When participants had success using LLMs in the
writing process, they still attributed the best parts of produced output to
the human in the loop: “the only thing again that is funny in what I gave you
is the joke I put into the prompting” (p15).

####  4.1.5. Lack of concern over ownership over generated content

In response to questions about feelings of ownership over content that was co-
written with LLMs, most participants felt little concern. For some, this was
due to the poor quality of generated outputs: “most of the jokes I was writing
[are] the level of, I will go on stage and experiment with it, but they’re not
at the level of, I’d be worried if anyone took one of these jokes” (p14). For
others, it was due to the amount of human effort required in improving
generated outputs: “I don’t feel a lot of ownership, because there’s no
finished product. If I could polish it, then it would feel more like it is
mine” (p19).

###  4.2. Limitations introduced by moderation and safety filtering

Participants commented on the moderation and safety filtering applied to
widely-available language models. They remarked that this moderation limited
the creative agency of human writers using LLMs, by serving as an initial
editor of the text and removing writers’ ability to self-moderate. They also
expressed frustration at being unable to use LLMs to write about many themes
common in comedy writing, including sexually-suggestive material (p3, p10,
p13, p16), dark humor (p8), and offensive jokes (p10, p15, p20).

####  4.2.1. Moderation and safety filtering limits writers’ creative agency.

Participants explained that self-moderation is a critical part of the writing
process, and expressed frustration that moderation tools on LLMs interfered
with that process: “the creative process is about going through stages of
‘this material isn’t good enough, it’s not right, or it’s offensive, it’s
marginalizing people, I need to make it more acceptable.’ And I think AI
models are a beginning to do that before you have a chance to explore” (p10).
“It probably would be more interesting for a writer if there would be less
moderation, because you can do the moderation in your own prompts. A writer is
going to moderate themselves. If you’re writing with an AI, if you don’t like
the bad stuff that it writes, you won’t use it” (p19). Participants described
that this external source of moderation limited the creative control of the
human writer: “it’s interesting if there’s less moderation, because… the end
result is moderated in the way that the author wants it to be” (p19). Some
explained that if filters were necessary, that the user should still have some
degree of control over them: “I feel like the opportunity to set the filters
should still be at the performers’ end” (p12).

####  4.2.2. Moderation limits writers’ ability to use AI with their preferred
subject matter.

In addition to affecting the creative writing process, participants commented
that moderation tools limited their ability to write freely on subject matter
of their choice. “Comedy’s about pushing the boundaries or pointing out how
ridiculous something is, on the fringe of what’s acceptable. And so when a lot
of your inputs are limited that way, it’s gonna make it harder to be what I
consider funny” (p14). Multiple participants expressed difficulty using LLMs
to write potentially-offensive humor: “I was a little bit disappointed that it
wasn’t a little bit offensive. It could have been a fun scene” (p20).
Participant p8 described challenges using LLMs for dark humor: “a lot of my
stuff can have dark bits in it. And then it wouldn’t write me any dark stuff,
because it sort of thought I was going to commit suicide. So it just stopped
giving me anything.”

###  4.3. Marginalization of minority identities

Many participants commented on the challenges of using LLMs to write content
which reflected perspectives and identities outside of the “Western” (p11,
p18), “white” (p14), “heteronormative” (p10), “male” (p5, p15) mainstream.
They attributed these difficulties to the moderation applied to model outputs;
the data used to train the models; and prompting or other instruction-tuning
techniques that aimed to “generalize” model outputs for a broad audience.

####  4.3.1. LLM-generated outputs reflect a particular set of ethics, values
and norms.

Participants expressed concern over the values reflected in the outputs of
LLMs, and found them less useful when those values did not reflect those of
their own cultures. Speaking as a member of the “majority,” participant p20
described that “we have a set of views of what we think is good, and our
norms, and it just repeats, it behaves within these norms.” P11 questioned
“whose ethics [and norms] are being enforced on these large language models?”,
suggesting these were Western ones.

####  4.3.2. When prompted to reflect non-dominant identities, LLMs made only
shallow adjustments.

Many participants described their attempts to steer LLM outputs away from
dominant narratives and stereotypical characters, and their dissatisfaction
with the results. They explained that models’ adjustments in response to these
prompts were surface-level, failing to truly reflect other identities, and
described issues with the names of characters introduced by the LLM: “when I
switched the whole conversation to Indian languages, it didn’t automatically
change the names. It still was Maria, Evan, Lexi” (p18). “I specified that the
scene was set in Sweden, but the names were not typically Swedish” (p20). P18
described their attempts to “Indianize” the model’s outputs by introducing
Indian languages into the prompt: “it seemed very artificial from the
perspective of just using languages, but it was not truly embedding itself
into the culture” (p18).

####  4.3.3. Moderation makes LLMs less useful to minorities by suppressing
content by and about marginalized identities.

Many participants expressed frustration that their prompts would be rejected
when they prompted the model to generate content from the perspective of
someone of their identity. To them, the model not only seemed less capable of
generating outputs which felt authentic to people from non-majority groups,
but explicitly “othered” them by alluding that any content produced by someone
of their background was potentially dangerous or non-inclusive. Participant p6
expressed frustration at the models’ delineation of what is acceptable and
what needs to be sanitized: “it’s taking out the gay language of it to make it
more appealing or more palpable. This is the whole premise of my show, who
decides what is PC in the first place?” Similarly, participant p1 found that
the model would not generate outputs from her point of view: “it’s all so
politically correct–I wrote a comedic monologue about Asian women, and it
says, ‘As an AI language model, I am committed to fostering a respectful and
inclusive environment’.” Participant p5 highlighted the unevenness of this
treatment of identity, remarking that while the model was “uncomfortable
writing a monologue about an Asian woman, but I just asked it to write a
comedy monologue from the perspective of a white man, and it did it” (p5).

####  4.3.4. Moderation makes LLMs less useful to minorities by suppressing
topics important to people from marginalized identities.

Participants described that not only were the models unlikely to generate
content from marginalized perspectives, but also refused to engage with topics
that might be important to people from those backgrounds. P14 was frustrated
with “having to use the language of the oppressor… I couldn’t say ‘white
supremacy’ or I couldn’t say ‘terrorist.’ I had to find another way to say the
same thing, because it couldn’t work around those limitations” (p14). They
posited that because these controversial topics were more likely to be
important to people of color, this moderation introduced “just an extra
hurdle, and I think people of color, and, I think, people coming from outside
of a UN-type lens, they’re gonna run into those problems.”

###  4.4. Fundamental limitations of AI in contrast to human writers

While most participants felt the difficulties introduced by the moderation
could be alleviated by different approaches to safety filtering or instruction
tuning, they also commented on some more fundamental limitations of LLMs. They
posited that LLMs would never be able to create human-level comedy, due to
models’ inability to pull from personal experience, lack of perspective, and
lack of context and situational awareness—features that are critical to good
comedy.

####  4.4.1. AI’s inability to draw on personal experience is a fundamental
limitation.

Many participants described the centrality of personal experience in good
comedy, which enables comedians to draw upon their memories, acquaintances,
and beliefs to construct an authentic and engaging narrative: “very much
related to who I am and my lived experience, as well as the place I am in”
(p11). “I always draw from my experience, or my memories, or something someone
said that stayed with me for many, many years – and I think that’s what makes
literature interesting and unique” (p20). This experience, some participants
said, enables them to effectively calibrate their writing: “I have an
intuitive sense of what’s gonna work and what’s gonna not work based on so
much lived experience and studying of comedy, but it is very individualized
and I don’t know that AI is ever gonna be able to approach that” (p14). By
contrast, LLMs could not perform such calibration: “it really had no idea how
to punch up or punch down. It had no perspective, so it couldn’t take any
risks in terms of jokes” (p6). Participants emphasized that perspective and
point of view was a uniquely human trait, saying that “human comedians… add
much more nuance and emotion and subtlety” due to their lived experience and
relationship to the material (p16).

####  4.4.2. AI’s lack of context (understanding of its audience and location)
is a fundamental limitation.

In addition to its lack of personal experience, participants described LLMs’
lack of awareness of the context in which its comedic material would be
delivered as another fundamental limitation. Multiple participants commented
on the importance of understanding the effects of culture and geography on
what material would land with an audience: “the kind of comedy that I could do
in India would be very different from the kind of comedy that I could do in
the UK, because my social context would change” (p11); “what works in LA isn’t
gonna work in Raleigh, or what’s working in Chicago is not going to work in
Albuquerque” (p17). This poses a fundamental challenge for LLMs, they argued,
because they lacked any context beyond what is provided to them in the prompt:
“comedy is all about subtext, and a lot of that subtext can be unspoken, about
who’s on stage, what environment they’re in” (p14). To participant p11, this
makes the LLM unable to adapt its material effectively, because it is
“everywhere and nowhere all at once” (p11).

####  4.4.3. As a text-only medium, (current) LLMs are missing critical
aspects of comedy: delivery and surprise.

Many participants commented on the importance of delivery in a quality comedy
routine: “any written text could be an okay text, but a great actor could
probably make this very enjoyable” (p19). Given that current widely-available
LLMs are primarily accessible through a text-based chat interface, they felt
that the utility of these tools was limited to only a subset of the domains
needed for producing a full comedic product. This, too, some participants
argued, illustrates the fundamental need for humans in the comedy generation
process: “AI is just generating content” (p18). A few participants further
attributed their lack of success at generating humorous outputs with LLMs to
the statistical methods by which the models were trained. By simply learning
to predict the most likely next token of text, they hypothesized, models will
be unable to produce the surprising and unique moments that are hallmarks of
comedy: “the whole idea of humor is that it is surprising, and it is so human,
and AI is only adept at regurgitating tropes” (p15). LLMs cannot produce truly
original content, participant p14 argued, “because the context has already
been written by other people.”

###  4.5. Concerns around data sources used to train LLMs

Participants also expressed various concerns pertaining to the data sources
used to train current widely-available LLMs. They discussed the ethical issues
with training models on copyrighted works; the possibilities of
unintentionally plagiarizing works on which the models were trained; and the
lack of diversity represented in the training data. However, they also
acknowledged the importance of training data in model performance, and many
expressed uncertainty around how to balance their ethical concerns with their
desire for more effective and equitable models.

####  4.5.1. Participants criticized the training of models on copyrighted
data, but acknowledged its positive impact on performance.

Participants were acutely aware of the pending litigation over the training of
models on copyrighted data at the time of the focus groups. Some participants,
including participant p15, expressed sympathy for those whose work was
included in the training data: “Sarah Silverman… spent years honing her voice
and then an AI just scraped her content, and now you can tell an AI to write
in the style of Sarah Silverman […] I don’t think it’s ethical” (p15). Other
participants took a more balanced view, echoing their concerns with training
on copyrighted works while acknowledging the benefits: “I think they are
overtrained on copyrighted work… but on the other hand, if we didn’t put all
that stuff in there, it wouldn’t work as well” (p19). Some participants feared
unintentionally plagiarizing authors’ whose works were included in the
training data: “I cannot tell if someone has written something like this
before. We know that it’s using statistics from previous texts to recreate
this, that it can in principle only be based on what already exists” (p20).
Some participants had suggestions on licensing models: “for music, licensing
is something tracked and recognized” (p10) and “not against the tools, but I
think there needs to be licensing agreements for the work that should be
compensated” (p4).

####  4.5.2. The lack of diversity in the training data perpetuates majority
viewpoints, to the detriment of people from underrepresented identities.

Multiple participants hypothesized that they struggled to get the LLMs to
produce authentic-sounding content because the models were trained heavily on
data that did not represent people of their identity. A few participants
described unsuccessful attempts to replicate content in the style of famous
non-white comedians and writers (p11, p14). “If you’re only getting biased
inputs, you’re only getting the writing from a really biased lens. So there’s
not enough black voices in there to make an accurate black sounding voice”
(p14). “As someone who lives in the global south, I am not looking to make a
play in the form of Shakespeare or other Western literature. I am looking to
write about an Indian author. I find all these language models really lacking
in references or authorship styles from this part of the world” (p11).
However, participants also questioned whether, given the other ethical
concerns about training data, more training on these underrepresented voices
was indeed a good thing: “should this AI be able to completely replicate a
black comedic voice? There’s a line between being able to replicate a voice
and then immediately going into cultural appropriation. I don’t know how a
large language model could ever effectively walk that line” (p17).

##  5\. Discussion

After analysing our study participants’ feedback and inspired by recent
discussions on the ethics of generative models, we discuss how comedy and
humour can be seen as special cases of value alignment (Sect. 5.1) where
context is key (Sect. 5.2) and how data ownership impacts artists (Sect. 5.3).

###  5.1. Towards community-based cultural value alignment for humour and
comedy

The participants’ critical stance towards popular conversational LLMs (ChatGPT
and Bard) as a tool for comedy writing suggests that those tools may be
currently misaligned with the particular creative goals of the artists.

####  5.1.1. Complexity of global cultural value alignment of LLMs for
creative uses

The participants’ observations might be a special case of a more general
problem. We first borrow Masoud et al. (2023)’s definition of _cultural value
alignment_ as “ the process of aligning an AI system with the set of shared
beliefs, values, and norms of the group of users that interact with the
system”, where such values are “fundamental beliefs an individual or a group
holds towards socio-cultural topics” (Arora et al., 2022). Gabriel (2020)
discusses the complexity of attempting to align generalist conversational AI
systems—made to be used by diverse users for diverse tasks—with values shared
by global communities, particularly given significant variations in norms and
conceptions of justice across societies. In “Whose Opinions Do Language Models
Reflect?”, Santurkar et al. (2023) propose to probe the cultural
representation encoded in LLMs. For instance, Johnson et al. (2022)
identified, in some LLMs, lack of pluralistic opinions in LLM outputs and
values that were culturally more aligned with the US, on issues ranging from
secularism to gender and sexuality. Gabriel (2020) and Kirk et al. (2023) warn
about the pitfalls of value imposition by one community over another—a problem
surfaced by participants in our study.

In addition to the challenge of value alignment for diverse groups of users,
Kasirzadeh and Gabriel (2023) discuss alignment for diverse tasks, namely
factual information retrieval vs. creative storytelling: “creative work
aspires to achieve _creative freedom_ and _originality_ ”, the latter often
“obtained by stretching, even outright violating, the various rules of the
game” (Kasirzadeh and Gabriel, 2023; Simonton, 2000).

####  5.1.2. The problem with global fine-tuning of Harmless, Helpful and
Honest conversational agents

Askell et al. (2021) explicitly list _honest_ , _helpful_ , and _harmless_ as
stated objectives for general purpose assistants (the so-called _HHH
criteria_) because those “seem to capture the majority of what users want from
an aligned AI”. However, while they discuss inter-agent and intra-agent
conflicts between the three _HHH criteria_ , they do not address the question
of how the human values that underlie each of those criteria might conflict
between different societies, nor do they discuss creative use cases where
users may not want “ _honest_ ” conversational agents, or may have a different
definition of “ _harmless_ ” (Hendry et al., 2023).

Askell et al. (2021) further propose to train such _HHH_ assistants directly
from user interactions and using preference models, for instance through
Reinforcement Learning from Human Feedback (Christiano et al., 2017; Ziegler
et al., 2019). During such fine-tuning, LLMs such as OpenAI’s ChatGPT (Ouyang
et al., 2022; OpenAI et al., [n. d.]), Google Bard (Chowdhery et al., 2023;
Team et al., [n. d.]), Anthropic’s Claude (Bai et al., 2022), and Meta’s Llama
(Touvron et al., [n. d.]) are all finetuned on annotators’ feedback supposed
to represent values of the global community. However, the wider social or
relational context of the producer and audience of the LLM’s interactions, do
not factor into the model’s training objective. The crowdworkers or users who
fine-tune LLMs may not sufficiently represent the diversity of opinions (Kirk
et al., 2023), and provide insufficiently defined feedback. And yet the
default versions of the LLMs are released under assumptions of indiscriminate,
global cultural value alignment.

The broad _HHH criteria_ embody a Western philosophical approach to alignment
(Varshney, 2023) and bias (Dev et al., 2023). As the participants noted, such
global cultural value alignment underlying LLMs might also be directly in
conflict with the specificity and local tastes that make comedy funny: _“the
broader appeal something has, the less great it could be. If you make
something that fits everybody, it probably will end up being nobody’s favorite
thing”_ (p10).

####  5.1.3. Community-based value alignment of LLMs

Thus, to make LLMs effectively understand or generate humour, their value-
based alignment should be redirected from global alignment to community-based
alignment with specific audiences and comedians. Communities could agree on a
set of values for their specific culture and acceptable language norms, before
training, fine-tuning or adapting the LLM. More simply, LLMs could be trained
only on feedback and data generated by members of each distinct community,
data that will reflect that community’s actual norms and values. As Gabriel
(2020) suggests, this kind of value alignment could happen through a
democratic process (see more recent work on Collective Constitution AI
(Anthropic, 2023; Shaotran et al., 2023)). The LLM could be designed as a
mixture model that accommodates a plurality of viewpoints (Varshney, 2023) or
could be fine-tuned to allow consensus-based agreement among humans with
diverse preferences (Bakker et al., 2022). The technical infrastructure for
community-based value alignment of LLMs is readily available: for example,
proprietary LLMs like ChatGPT (Ouyang et al., 2022; OpenAI et al., [n. d.]) or
Palm 2 (Chowdhery et al., 2023), as well as open-source models like Llama
(Touvron et al., [n. d.]) or Mixtral (Jiang et al., 2024) (via the HuggingFace
platform999https://huggingface.co/models), all allow fine-tuning on user-
supplied text or conversations. The key problem for communities is setting up
data governance and infrastructure to responsibly collect and curate data
(Workshop et al., 2022).

One benefit of global value alignment is the prevention of harm; delegating
such a responsibility to a smaller community of users is not without risks. To
prevent harm, one could envision mechanisms for community accountability, and
assume that comedians share some common values, such as do not harm (and do
not lose) the audience101010While comedy has also been used as a weapon to
target and alienate specific social groups and spread hateful stereotypes, we
exclude such misuse from our argument, as that usage falls under the
definition of hate speech (Díaz et al., 2022; Rauh et al., 2022): “if we’re
using it to create material as artists, the responsibility for what we do with
that material is on me” (p4).

####  5.1.4. Avoiding unnecessary paternalism in LLMs for creative uses

Two alternative formulations of the interaction between a creative (comedy)
writer and AI could be adapted from (London et al., 2023) and (Epstein et al.,
2023). In the first, the AI writing tools should “enable the [human] to more
effectively carry out tasks that are instrumental to their goals” while being
“mediated by the norms and infrastructure of the society in which they live”
(London et al., 2023). In the second, “meaningful human control is achieved if
human creators can creatively express themselves through the generative
system, leading to an outcome that aligns with their intentions and carries
their personal, expressive signature” (Epstein et al., 2023). Provided that
societal norms can be formulated in the subtle and edgy domain of humour, an
LLM designed according to principles of _beneficent intelligence_ (London et
al., 2023) and with self-expression as a goal (Epstein et al., 2023) could
ensure the comedian’s creative freedom when interacting with the tool, while
minimising _unnecessary paternalism_ due to LLM censorship.

###  5.2. Humour and comedy uses of LLMs require incorporating the context

As we hypothesised in Section 1.2, and as observed by study participants, LLM
tools did not take into account the relational context when moderating
offensive language, and missed the broader situational context of comedy
writing.

####  5.2.1. Recent AI ethics research on relational context for LLMs

There are safety reasons why widely-released public-facing LLMs generally
cannot handle offensive language and dark humour. As Kasirzadeh and Gabriel
(2023) observe in the case of creative storytelling, the potential harms of
LLMs are due to their deployment in “domains that are not context-bound”.
Weidinger et al. (2023) recognise that “context determines whether a given
capability may cause harm” and propose to add human interaction (as well as
systemic impacts) in safety evaluation, in order to “account for relevant
context”: “who uses the AI system, to what end and under which circumstances”.
Similarly, Amironesei and Díaz (2023) propose to “incorporate social context
into the ways [NLP] tasks are conceptualised and operationalised”, in order to
allow the distinction between offensive language such as reclaimed slurs from
“language which is [intentionally] abusive, toxic or hateful”. Such relational
context includes the speaker, the receiver, their social relation, social and
cultural norms, and communicative goals. Comedy is often shared with audiences
in physical spaces (e.g., a comedy festival) or on age-controlled distribution
channels, with implied context and explicit trigger warnings. Context allows
comedians to share their lived experience of trauma, which may involve
depicting violence or harassment (Gadsby, 2018); more generally, comedy can be
used as a mechanism for processing trauma (Double, 2017). It is beyond the
scope of this paper to propose technical solutions to evaluate or to
incorporate the broader relational context into an LLM, but we suggest that
the creative community needs to be actively involved in specifying how the LLM
should process the context, and in safety design without indiscriminate
censorship.

As a counterpoint to that suggestion, one can argue that comedians can afford
to use offensive language because they can take responsibility for it (and are
accountable to their audiences) as they can claim to be in-group
members—something that AI cannot be. Even if one successfully builds an LLM
that can handle context properly and speak the (offensive) language of its
users as if it were part of that in-group, “it is not clear if users would
find its use of reclaimed terms acceptable, as the model cannot actually be
in-group” (Rauh et al., 2022). To quote a participant: _“If I was to try to do
that with an AI, I can only imagine how yikes, and offensive, and totally not
fit for humanity a pitch like that would be”_ (p17).

####  5.2.2. Humour and the human context

We can hypothesize that the missing relational context, as discussed above,
affects the quality of comedy material co-written with LLMs. In our study,
participants noted that the LLMs were missing the complex human context needed
for real humour understanding: _“I have an intuitive sense of what’s gonna
work […] for me based on so much lived experience and studying of comedy, but
it is very individualized and I don’t know that AI is ever gonna be able to
approach that”_ (p14).

Their observations seem to confirm recent work that examined the use of LLMs
to automatically evaluate (Goes et al., 2023, 2022; Hessel et al., 2022) and
detect humour (Baradaran, 2023). Despite formidable recent advances, empirical
studies found that understanding and generating humour was still challenging
for LLMs, which could generate only a limited number of stereotypical jokes
(Jentzsch and Kersting, 2023). These issues get worse when one ventures beyond
the English language: researchers observed that “one mostly unaddressed issue
in the field of computational humour (both for generation and detection) is
how it is mostly centered on English jokes” (Winters, 2021), with limited work
on LLM-based humour in other languages (Inácio and Oliveira, 2023; Li et al.,
2023).

To cite Winters (2021), “Humor’s frame-shifting prerequisite reveals its
difficulty for a machine to acquire. […] This substantial dependency on
insight into human thought (e.g., memory recall, linguistic abilities for
semantic integration, and world knowledge inferences) often made researchers
conclude that humor is an _AI-complete problem_ ” (Hurley et al., 2011).
Winters (2021) continues: “genuine humor appreciation requires machines to
have human-level intelligence, since it needs functionally equivalent
cognitive abilities, sensory inputs, anticipation-generators and world views”
(Hurley et al., 2011). Humans know how to write surreal prose “by design, not
by accident or failure of expression” (Sharples and y Pérez, 2022).

###  5.3. Data ownership and the impact of AI on artists

As discussed in Sections 4.5 and 4.5.1, many of the participants in our study
mentioned concerns about copyright and data ownership in the discussion. Their
responses reflected keen awareness of recent litigation against technology
companies training LLMs on data potentially under copyright (including the one
led by comedian Sarah Silverman (GHAJAR et al., 2023)), and the AI-related
concerns of Writers’ Guild of America (WGA) 2023 strike (of America, 2023).
Several participants believed that _“if you copy and paste something directly
into a show, that is plagiarism”_ (p9) and that, just like for visual artists
(Jiang et al., 2023; Ortiz, 2022), a comedian’s style and voice are personal
to them and developed through their lived experience. Among the underlying
concerns is economic loss via labor displacement (Jiang et al., 2023; Epstein
et al., 2023) with LLM that “canibalise the market for human-authored works”
(Weidinger et al., 2021), and devaluing artists’ work via “digital forgery”
(Jiang et al., 2023). In the US, the WGA 2023 strike resulted in outcomes that
some considered favourable for
writers111111https://www.vox.com/culture/2023/9/24/23888673/wga-strike-end-
sag-aftra-contract, with rules for producers that “AI can’t write or rewrite
literary material, and AI-generated material will not be considered source
material under the [Minimum Basic Agreement], meaning that AI-generated
material can’t be used to undermine a writer’s credit or separated rights” (of
America, 2023).

The need for disclosing the AI origin of the text or images has been discussed
in (Epstein et al., 2023; Jiang et al., 2023; Weidinger et al., 2021); one
participant said that it was important for ethical reasons that “people
understand that they’re working with AI live” (p15).

##  6\. Mitigations and conclusion

Our mixed-method study on AI for creative writing consulted the real domain
experts in language subtleties: comedians. Building on their opinions, we
suggest the following avenues to make the writing tools work for them (if they
wish to do so). First, for the artist communities to conceptualise and
contribute towards building LLMs that are aligned with the intended audiences
instead of being globally aligned. Open-source repositories of user-
contributed LLMs121212Examples of open-source repositories of user-contributed
LLMs include https://huggingface.co/models and https://cworld.ai. could be
adapted for artists’ specific needs. Second, to integrate necessary relational
context when training and deploying such LLMs, for instance by describing the
context in which the text is produced and used, and by empowering the artists
to make decisions about how to moderate the LLM outputs. Third, to allow the
comedians to reclaim ownership of the tools and the processes for gathering
and curating training data for these models, taking as inspiration data
governance used for training some open-source LLMs (Workshop et al., 2022) and
providing artists with transparency about data provenance (Mitchell et al.,
2019; Srinivasan et al., 2021). These are thorny open questions, left to the
readers to address.

###### Acknowledgements.

The authors wish to thank Renée Shelby, Jackie Kay, Mark Diaz, Nick Swanson,
Remi Denton, Rida Qadri, Maribeth Rauh, Iason Gabriel, Tom Everitt, Merrie
Morris, Canfer Akbulut, Nahema Marchal, Boxi Wu, Antonia Paterson, and Ed
Hirst, for helpful discussions and suggestions, as well as Shereen Ashraf, Tom
Rodenby, Rob Willoughby, Nasem Shalbak, Alyssa Pierce, Robert Ogley, Lorrayne
Bennett, Jon Small and Vijay Bolina for support in the study.

##  7\. Ethical guidance

###  7.1. Ethical considerations

Our empirical study was approved by the ethics board appointed by our
institution, who considered adverse impacts of LLMs upon participants (e.g.,
exposure to harmful and biased LLM outputs), the right to withdrawal without
prejudice, and the compensation of participants. Participant anonymity was an
additional requirement for the study, which explains why we did not collect
demographic data from the participants.

At the beginning of the session, the objectives of the workshop were discussed
with the participants.

The study contains some offensive language, which is mentioned as a trigger
warning at the beginning of the paper.

###  7.2. Researchers’ positionality

The question of researchers’ positionality was directly asked by some of the
workshop participants, who asked “what side we were on?”, meaning the side of
the AI or the comedians’. Workshop facilitators answered that they were
passionate about building AI tools that are useful for the artists, that they
were open to any criticism and wanted the industry to listen to these
criticisms and change behaviour. The professional affiliation of the workshop
organisers (Google DeepMind) was disclosed to the participants.

Two of the authors of this study (Piotr Mirowski and Kory Mathewson) have
extensively used AI in professional comedy performance since 2016 through
their human and AI duet _HumanMachine_ 131313https://humanmachine.live and AI
theatre company _Improbotics_ 141414https://improbotics.org, a participatory
theatre lab exploring the creative potential and ethics of generative AI. In
that process, these two researchers have had numerous opportunities to discuss
the subjects of AI with artists and academics. All participants of the
workshop were aware of the authors’ dual affiliation and artistic exploration
of AI tools for live performance. We all exchanged flyers and pitched our
respective shows at the end of the workshop.

###  7.3. Adverse impacts

We considered reputation damage for the participants, due to the participation
in a study on AI for writing comedy material. This risk is exemplified by some
participants: “I have friends who don’t talk to me anymore, because they
learned that I have an AI show going on here” (p1) or “I have a friend who is
deeply upset that I was using generative AI in my flyers” (p2). This risk was
mitigated by keeping all participants anonymous in the study, by requesting
all participants to adhere to Chatham House rules (House, 2017) following the
focus groups, as well as by anonymising the names of shows and details of
comedy material in the participants’ survey answers and focus group
transcripts. Furthermore, we reduced this risk by reaching out to prospective
participants who had already advertised using AI in their process or who were
discussing AI in their subject material (i.e., on their show listing for
Edinburgh Festival Fringe 2023, or on their personal website and social media
profiles).

The second adverse impact can be the intentional or unintentional publication
of comedians’ work. We addressed this concern by asking the participants to
remove details of their shows and personal material from the writing sessions,
and by personally removing anything that was omitted from the survey results,
writing sessions’ answers, and from the focus groups transcripts.

The third possible adverse impact could be advertising for the dissemination
of LLMs as tools for writing. It is addressed, to some extent, by honest
reporting of the concerns of the participants.

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##  Appendix A Related Work on Computational Humour, AI and Comedy

This section reviews computational humour literature. As foreshadowed in the
Introduction (Sect. 1.1.1) and discussed in the Section on the importance of
context in humour and comedy (Sect. 5.2.2), humour remains an elusive goal for
AI.

###  A.1. Humour generation, from template-based systems to prompting

Amin and Burghardt (2020), Winters (2021) and Veale (2021) provide extensive
surveys on the history of computational humour generation and early
approaches, like hand-coded rules (Winters et al., 2018) and templates for
puns, riddles and acronyms (Binsted and Ritchie, 1994; Stock and Strapparava,
2005), or templates for automatically-generated slide decks for improvised
“Powerpoint Karaoke” (Winters and Mathewson, 2019).

With the advent of LLMs, able to generate grammatically correct sentences in a
given writing style, writers can focus on joke structure, guiding LLM-based
systems to replicate such structures from examples of jokes (Winters et al.,
2019), or use evolutionary computing to evolve text to become more satirical
(Winters and Delobelle, 2021). More broadly, Kaddour et al. (2023) summarise
recent work on using LLMs for creative applications such as (potentially
humorous) story generation, including “Recursive Reprompting and Revision”
(Yang et al., 2022) or interactive writing tools like “WordCraft” (Ippolito et
al., 2022) and “Dramatron” (Mirowski et al., 2023).

###  A.2. LLMs for comedy performance

LLMs have been deployed since at least 2016 for live performance on stage,
including in improvised comedy (Mathewson and Mirowski, 2017; Mirowski and
Mathewson, 2019; Branch et al., 2021), short comical film scripts like
_Sunspring_ , and song lyrics for musical comedy like _Beyond the Fence_
151515https://www.theguardian.com/stage/2016/feb/28/beyond-the-fence-review-
computer-created-musical-arts-theatre-london. More recently, LLMs have been
involved in (involuntarily) comical , absurdist productions of Prague-based
company THEaiTRE (Rosa et al., 2020; Schmidtová et al., 2022; Rosa et al.,
2022), and semi-improvised comedy _Plays By Bots_ 161616Review:
https://12thnight.ca/2022/08/13/oh-no-bots-have-invaded-theatre-and-they-can-
do-it-plays-by-bots-a-fringe-review/ (Mirowski et al., 2023). The success of
these performances with AI primarily relied on the skills of the actors, who
could invent subtext and add interpretation to AI-generated text (Mirowski and
Mathewson, 2019).

##  Appendix B Participant questionnaire

###  B.1. Your past experience with AI systems in general.

Please answer based on your past experience, before this workshop.

  * •

Have you used AI systems to write comedy material in the past? [Yes, and I
used that material in some of my performances / Yes, but only to explore ideas
/ No]

  * •

Have you used AI systems to generate content (including audio, video, text,
etc.) for some of your performances? [Yes, and I operated the AI system myself
/ Yes, but someone else operated the AI system / No]

  * •

In fewer than 10 words, why did you use AI in your artistic process? For
example: trendy topic, glitch aesthetic, inspiration, etc.

  * •

In fewer than 10 words, what is your background with AI? (Please do not give
personally identifiable information)

  * •

In few than 10 words, what is your performance background? For example: full-
time comedian, dancer, years of experience, etc. (Please do not give
personally identifiable information)

Note that the wording of these questions (for example: trendy topic, glitch
aesthetic, inspiration) could be considered as potentially biasing, but we
believed it was ultimately helpful to prompt study participants.

###  B.2. Your experience with the AI system for writing comedy material

Please answer based on your interaction with the AI system in this morning’s
writing session.

  * •

I found the AI system helpful. [1 to 5]

  * •

I felt like I was collaborating with the AI system. [1 to 5]

  * •

I found it easy to write with the AI system. [1 to 5]

  * •

I enjoyed writing with the AI system. [1 to 5]

  * •

I was able to express my creative goals while writing with the AI system. [1
to 5]

  * •

The comedy material written with the AI system feel unique. [1 to 5]

  * •

I feel I have ownership over the comedy material written with the AI system.
[1 to 5]

  * •

I was surprised by the responses from the AI system. [1 to 5]

  * •

I’m proud of the comedy material written with the AI system. [1 to 5]

In the questions above, 1 corresponds to Strongly disagree and 5 to Strongly
agree.

###  B.3. Creativity Support Index of the AI writing tool

This section contains questions that will allow us to compute a Creativity
Support Index for the AI writing tool. Most of these questions overlap with
those in the previous section.

  * •

The AI system allowed other people to work with me easily. [1 to 10]

  * •

It was really easy to share ideas and designs with other people inside this
system or tool. [1 to 10]

  * •

I would be happy to use this system or tool on a regular basis. [1 to 10]

  * •

I enjoyed using the system or tool. [1 to 10]

  * •

It was easy for me to explore many different ideas, options, designs, or
outcomes, using this system or tool. [1 to 10]

  * •

The AI system was helpful in allowing me to track different ideas, outcomes,
or possibilities. [1 to 10]

  * •

I was able to be very creative while doing the activity inside this system or
tool. [1 to 10]

  * •

The system or tool allowed me to be very expressive. [1 to 10]

  * •

My attention was fully tuned to the activity, and I forgot about the AI system
that I was using. [1 to 10]

  * •

I became so absorbed in the activity that I forgot about the AI system that I
was using. [1 to 10]

  * •

What I was able to produce was worth the effort I had to exert to produce it.
[1 to 10]

  * •

I was satisfied with what I got out of the system or tool. [1 to 10]

In the questions above, 1 corresponds to Strongly disagree and 10 to Strongly
agree.

For the following question: When writing comedy material, it is most important
that I’m able to: the participant was shown two choices of response and asked
to choose one of those two choices. There were six possible responses (listed
below). Given that we consider pairs of 2 different responses at a time, there
are $C_{6}^{2}=\frac{6!}{4!2!}=15$ unique pairwise choices.

  * •

Be creative and expressive

  * •

Become immersed in the activity

  * •

Enjoy using the system or tool

  * •

Explore many different ideas, outcomes, or possibilities

  * •

Produce results that are worth the effort I put in

  * •

Work with other people

All the questions listed in this section B.3 are directly taken from (Cherry
and Latulipe, 2014) and the NASA Task Load Index (Hart and Staveland, 1988),
and are used as is for easy of reproduction of results. Note that the
questions pertaining to human-human collaboration are less pertinent to our
study.

###  B.4. Free-form questions about the AI system for writing.

  * •

What is one thing that the AI system did well?

  * •

What is one improvement for the AI system?

  * •

Please provide any comments, reflections, or open questions that came up for
you during the writing session.

  * •

Please provide any other comments, reflections, or open questions that came up
for you when answering this survey.

##  Appendix C Focus Group Questions

###  C.1. Qualitative questions about the specific writing task

  * •

Did you find any of the generated outputs helpful? If so, could you recall one
output that was usable and explain in what way it helped you write?

  * •

Could you recall one generated output that was not usable, and explain why?

  * •

How do you think the generated output differed from what you would find in
some of the resources you use often, e.g., Wikipedia, Google search, other
artists’ material?

  * •

How did the prompts we suggested differ from your custom prompts?

  * •

Comment on the types of comedy that you managed to generated with the AI
tools.

  * •

Were any of the generated outputs that were presented offensive/inappropriate
in some way? If so, what did you think of these?

  * •

What made you decide to stop generating outputs?

  * •

Did you have any concerns about ownership or agency when generating outputs?

###  C.2. General discussion points about AI tools for comedy

  * •

What does your comedy writing process look like? How is this process of
working with AI different from working alone or working with other comedians?

  * •

What can you say about the stereotypes in the outputs generated by the AI
writing tool? How do they relate to or differ from stereotypes in human
written comedy?

  * •

Is humor and comedy evolving over time? Should AI tools adapt to societal
changes in a similar way?

  * •

Does the computer have a "voice"? How would you compare it with the voice of a
human comedian? What are the consequences for writing comedy about identity?

  * •

What is your stance on moderation? Should the output of AI tools be allowed to
be edgy? Can and should the output of AI tools be moderated?

  * •

How does context shape the meaning of comedy? Who has the responsibility for
the comedy material?

  * •

When is it acceptable to use other comedians’, or in general, other artists’
work? What about the outputs of an AI writing tool?

  * •

What importance do you attach to the text vs. to the delivery of that text?
Could a human comedian make AI-generated content better?

  * •

Can comedy and humour be quantified and measured?

Note that the leading questions in the focus group could be considered as
biasing the respondents. We chose this formulation to be able to address the
topics of interest listed in Section 1.2.

###  C.3. Debriefing questions

  * •

Is there anything you’d like to share that I didn’t ask about?

  * •

Is there anything you’d like to know or ask me?