Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction

2.1 The Practice of ML in the Arts: Situated Perceptions, Politics, and Use of Technology

The practice of ML in the arts is part of a longer history of art-and-technology dating to the era of mainframe computing when the first “purely aesthetic image [was] made on a computer” [59, p. 39]. The earliest connections between ML and art date to the 1970s when Harold Cohen developed the rule-based ML-art algorithm AARON [19]. While artists continued to create and exhibit conventional ML artwork provoking questions about relationships between society and technology (e.g., [52, 122]), the development of neural networks — and specifically Generative Adversarial Networks (GANs) in 2014 — marks a pivotal moment in ML-mediated art. GANs generate new content (e.g., image, text, audio) based on the data it was trained on [44]. As developers open-sourced GANs, artists shared resources and techniques about ML-mediated creativity [3]. Making sense of how different artist communities experience ML models thus requires attention to their socially situated context of use [32, 121]. While ML-art communities cohere in their engagement with computation, the varied politics of art worlds and artist motivations “fractures that technocultural material into millions of heterogeneous interests and agendas, specific investigations, aesthetics, approaches, and projects” [124, p. 5]. Consequently, it is important to examine the situated knowledge and beliefs of different artist communities.

Far from cohesive, various art-and-technology communities hold different understandings of the relationship between technology and art, from a depoliticized embrace of technology to a critical stance on the social impacts of technology on society [81, 84, 114, 121]. Prior HCI scholarship examines the techniques [71, 104] and politics [15] of critical ML-art communities in terms of how artists perceive and materially engage with models. Artists who employ ML in their creative practice often use appropriation and experimentation in ways they perceive as countercultural to dominant ML engineering values [32], such as accuracy, productivity, and performance [15, 98].

Moreover, ML-artists characterize ML models and data as creative material to be reworked through their practice [104]. As Caramiaux and Alaoui [15, p. 9] emphasize, artists “wor[k] with AI…through a concrete experience of the algorithm’s behaviors rather than a theoretical understanding of its capabilities.” These studies characterize this orientation to creative ML as a “craft approach,” where artists approach code and ML models as material to be (re)shaped and (re)formed in creative practice [71, 104]. This “craft approach" encompasses manipulating training datasets, altering algorithms, and developing new AI models. Here, the practice of ML in the arts is characterized by iterative engagement with models as a process and material [99], embrace of the unpredictable nature of models characterized by errors and glitches, and reframing ML as an instrument for creation [15]. In short, ML-artists engagement with ML reflects their situated use and appropriation of these algorithms, raising questions about how off-the-shelf creative ML, such as T2I models, shift the conversation.

2.2 T2I Models, Sociotechnical Harms, and Harm-Reduction in Responsible AI Practice

Text-to-image (T2I) models allow users to create photorealistic images from open-ended text prompts [89, 91]. The release of beta T2I products in mid-2022, such as Stable Diffusion [1], Midjourney [77], and DALL-E-2 [82], made technologies previously accessible to researchers and artists in limited capacities, available to wider publics. Without learning to manipulate code, illustrate, paint, or photograph, people can generate high-quality, complex images comparable to an experienced artist [75, 94]. This catalyzed discussion among artist communities about T2I-mediated creativity, including discourses of how T2I might expand [47, 85] or erode creative pursuits [64, 74]. Whereas some artist communities embrace T2I tools (e.g., the “promptism" movement [16, 57]), others have banned its use in art forums, competitions, and conventions [4, 33, 86]. A focus of public discussion among artist communities and researchers is the harm to creative practice (e.g., [20, 66, 97, 126]). These conversations include questions of consent [7] and use of artist’s work in training data [34, 109], global legal deliberations regarding copyright [86, 93], compensation [78], and macro-economic harms to creative labor, including job displacement [18]. Alongside these discussions are increasing social and regulatory expectations that ML models and products will be developed “safely and responsibly" [21, 54], which requires understanding of contextual use.

As a field, responsible AI is concerned with developing strategies to reduce sociotechnical harms from algorithmic systems [9, 87, 90, 102, 106], that is the “adverse lived experiences resulting from a system’s deployment and operation in the world" including computational and contextual harms [105, p. 723]. However, the field has been critiqued for its emphasis on computational harms and technical problems in model pipelines influencing the representational politics of generated content [10, 63, 105]. Much responsible AI research focuses on examining the ability of T2I models to reproduce demeaning stereotypes that reinforce unjust social hierarchies along intersecting axes of race [6], disability [42], and geopolitical cultures [5, 88, 118]. Common strategies to address computational harms focus on model-level content moderation strategies, such as implementing safety classifiers that restrict model inputs or outputs, blocklists, and training data remediation [48]. While addressing the representational politics of T2I models is an important area of research, what constitutes harm and effective harm reduction interventions are contextual and require an understanding of their different situated uses.

When questions of sociotechnical harm and harm reduction are discussed, responsible AI scholars recognize the importance of engaging communities to surface meaningful intervention touchpoints [8, 61]. Recent responsible AI approaches to harm identification and reduction for T2I models advocate bringing in stakeholder voices through qualitative engagements that draw attention to the ML product lifecycle [92] and center communities as experts in articulating how technologies replicate systems of social power [88] that are often challenging for developers to see due to “privilege hazards" [31]. However, there is room to strengthen community engagement in responsible AI research through analytical lenses, such as folk theories [28, 108], that enable a rich understanding of users’ situated knowledge.

2.3 Folk Theories and User Knowledge about AI

Algorithmic folk theories are the beliefs that users — with different levels of expertise and experience — develop to explain the outcomes, effects, or consequences of algorithmic systems [29, 45]. While HCI employs various definitions and methods to surface and analyze folk theories, the field is aligned in the utility of folk theories as an analytical lens for understanding user beliefs, or knowledge, about algorithmic systems [30].¹ As Willett Kempton [65, p. 75] describes, “the word ‘folk’ signifies both that these theories are shared by a social group and that they are acquired from everyday experience or social interaction.” As such, folk theories are a malleable way users of algorithmic systems make sense of a system in relation to sociocultural dynamics [28, 62, 100].

Importantly, communities may hold “complex, multi-part folk theories" [28, 30] about a given algorithmic system that shapes how they interact with it [108] and understand developer practices [62], such as data collection [72]. Folk theories also (de)motivate the use of technology [62] and shape how communities employ [117] or resist [29] technology affordances. The folk theory analytical lens thus enables useful insights to understand how people experience algorithmic technologies in their lives and social relations [2, 108, 129], which can inform responsible AI practice.

Folk theorization influences how people form different relationships to technologies shaped by users’ algorithmic awareness, motivations, and imaginaries [26, 72] and enact agency considering their working knowledge of a system (e.g., [23, 28, 29, 67]). Put differently, they illuminate one dimension of algorithm-culture relationships that “situate people’s systems of thought and practices within the specific cultural conditions in which algorithmic use takes place” [107, p. 61]. Depending on the context, the social meaning and power users’ ascribe to algorithms may vary, including theorizing “the work” of algorithms as “confining, practical, reductive, intangible, and exploitative” [129, p. 807]. In their study of Spotify, for instance, Siles and colleagues [108, p. 11] find different folk “theories provide users resources to carry out strategies of action through which they enact different modalities of power and resistance.” In this way, folk theories illuminate user knowledge and how this is entangled with broader social discourses and algorithmic cultures [101].

In sum, algorithmic folk theory literature underscores how attention to user knowledge enriches understanding of how differently situated communities experience algorithms, which can illuminate both problems in algorithmic design and presentation [29] and from what standpoints they formulate knowledge [26]. Responsible AI literature underscores the importance of engaging with and understanding community viewpoints to surface grounded intervention points [88]. However, the field still grapples with questions about using ML in creative practice, potential harms, and harm reduction. To date, no studies examine ML-artist folk theorization related to these dimensions. Focusing on ML-artists who use T2I in this study, we sought to understand their beliefs toward using T2I models in their creative practice, harms from use, and corresponding harm reduction strategies.

3.1 Workshop as Method

We conducted three semi-structured workshops as part of a two-day engagement with 15 artists who incorporate T2I models in their creative practice. We engaged artists as a “community of practice" [123] with shared social and material interests [24] in ML-mediated art. We chose a participatory workshop methodology (e.g., [49, 70, 95]) to engage artists and elicit conversation around their situated knowledge and experience with T2I models.

Participatory workshops offer a method and instrument to animate participants’ social alignments [95], fostering “opportunity for knowledge exchange between researcher and participant” [49, p. 3]. In this study, workshop as method allowed for lively discussion and interaction among participants in a collaborative environment. These were not co-design workshops for developing a prototype, feature, or design recommendations [112]; we characterized them as workshops to participants given their multimodal nature and collaborative activities to formulate sociotechnical harms and reduction strategies.

3.2 Participant Recruitment

To recruit participants, we used a research partner, Google Arts and Culture, which is a non-commercial institute that works with global cultural organizations and artists.² We also employed snowball sampling [83], as interested participants shared study information with others in their networks. To participate in the study, participants needed to (1) identify as an artist, (2) be familiar with T2I models, (3) have incorporated T2I models in their creative practice, and (4) be 18 years old or older. We did not have specific quotas, yet aimed to recruit a geographically diverse group of artists, including those with and without formal training (i.e., self-taught artists), and thus did not use that as exclusion criteria.³

Recruitment began in early October 2022. Of the 26 candidates contacted to participate, 15 were accepted. The participants included six women and nine men from France, Hong Kong, India, Kenya, Poland, Sweden, Switzerland, the U.K., the U.S., and Zimbabwe. Participants completed an informed consent form before participating in the workshop. We offer high-level information about the artists’ self-described background to prime our Findings (Section 4); to preserve their privacy, we do not detail information about each artist.

3.3 Workshop Structure and Activities

The artists were invited to participate in a two-day series of structured, participant-directed educational talks and workshops comprising open-ended questions and activities (Table 1). Workshop activities were conducted in English; all participants were fluent. The event was held virtually to accommodate global participation and recorded with consent. The first day began with group introductions, inviting them to share information about their creative practice and where they work. This was followed by educational presentations to ground participants in a common language to aid discussion, with extensive time to discuss the impacts of these models on creative practice.

On the second day, we held the workshops, lasting 75-90 minutes, with 15-minute breaks between sessions. Workshop activities were chosen based on prior research workshops [95] and authors’ experience conducting similarly structured research with communities of practice, in which creating space for individual activities and group discussion cultivated rich insights. Five people, including the authors, facilitated the sessions.

3.4 Data Analysis

Three sets of data were collected from the workshops, including the transcribed video recording, facilitator notes, and workshop activities captured into the online whiteboard. We analyzed data in parallel using Reflexive Thematic Analysis (RTA) [11, 12]. Our use of RTA was informed by constructivist approaches foregrounding how people construct situated knowledge about technologies [50, 68] and our RQs outlined in the Introduction (Section 1). RTA’s theoretical flexibility opened the possibility for inductive analysis of artist practices and viewpoints that could be informed by frameworks concerned with understanding user knowledge and reasoning about technology interactions, expectations, and practices, which in our case was folk theories. Thus, our analysis focused on ML-artists’ beliefs about T2I tools and responsibility guardrails.

Two authors conducted data analysis (January—May 2023), independently coding the three data sources and developing themes. Data sources were iteratively read by the first and second authors to become deeply familiar with the data before coding. Initial codes, which are “analytically interesting idea[s], concept[s] or meaning[s] associated with particular segments of data [13, p. 53], were derived free-form, using qualitative analysis software (NVivo 12). Themes, “pattern[s] of shared meaning organized around a central concept" [13, p. 77], were recursively developed from codes. The two authors held four rounds of iterative discussion, moving from open codes to thematic discussions and resolving disagreements.

As we developed our analysis, we held folk theories as a potential overarching theme or structuring device, as we interpreted data reflecting their perceptions about the use and relationships of T2I models in creative practice. In the second stage of analysis, the authors collaboratively developed the eleven folk theories through interpretation and using coded extracts to revise and refine themes, looking for participants’ explanations about using T2I in creative practice, attitudes about harm, and what harm reduction practices or interventions are important to them. A number of rough thematic maps were used to develop the folk theories, and the analysis was iterative. In the final stages, we named the folk theories to capture their “essence and analytical direction" as part of the RTA process [11, p. 112]. Lastly, the two authors confirmed the findings with the third author.

3.5 Author Positionality

Our team comprises researchers with diverse disciplinary expertise, including responsible AI, machine learning, computer engineering, and Science and Technology Studies. In addition, two authors have experience working in and supporting visual arts communities: one author previously worked in a design studio in the U.S. South; another author has organized workshops on ML-art in the computer vision community. All authors — and the two additional facilitators in the breakout groups — currently work in institutions in the Global North, and have experience shaping ML pipelines from responsibility and equity-oriented standpoints.

The research team relied on our scholarly and professional experiences during the analysis, which was especially generative in discussing experiences with ML pipelines, responsible AI practice, and creative practice. While the research team drew on our academic and professional experiences during the analysis, we reflexively challenged our assumptions and interpretations during the analysis. In particular, we conducted this analysis amidst the rising popularity and associated critique of T2I tools in the public sphere. We reflected on both sides of what is an oft-polarized debate about creative ML to understand to what extent these discourses were present in the data.

3.6 Limitations

Although this study offers critical insights into ML-artist folk theorization, it has limitations. Our study focuses on the perspectives of artists who have or currently employ T2I models in their creative practice and thus reflects the perspectives of those who likely hold more favorable views toward T2I models. As such, this study does not capture the folk theorization of artists critical towards using T2I models, artists who do not have access to these models, or artists whose practice does not include digital art. Although our recruitment through a research partner enabled us to convene global artists who live and work in different cultural contexts, it limited the initial pool of artists to those with direct connections to the organization or those who have relationships with someone with a direct connection.

Moreover, our study design focused on understanding the perspectives and beliefs of one particular artist community and did not enable analysis across differently situated artist communities, such as (1) those who are unfamiliar with T2I models, (2) familiar but have not employed T2I models, and (3) those who have incorporated T2I models in their practice. Exploring how folk theories align or differ across differently situated communities and artistic intentions is a fruitful area for future research. Such work could potentially inform design or feature recommendations to develop T2I tools that better serve a wider range of artist communities.

4.1 Folk Theories of Use: Juxtaposing Multiple Uses of T2I in Creative Practice

ML-artists engaged in a range of folk theorization focused on two dimensions of “use.” (see Table 3). The first dimension concerns the divergent ways T2I models are employed in creative practice as a (1) creative medium with specific properties that can be crafted and molded and as a (2) mundane tool for executing and facilitating aspects of their creative projects. The second dimension contextualizes multiple meanings of creativity. Here, they articulate distinctive theories clarifying while T2I tools (3) increase access to creative modalities, (4) true “creativity” requires rising above basic model affordances. These overlapping folk theories underscore how the ways ML-artists make sense of T2I models are contextualized by the motivations, perspectives, and goals of the specific user.

4.1.2 Use Folk Theory 2: T2I Models are a Mundane Tool.

ML-artists also conceptualize T2I models as a mundane creative tool to prototype ideas and communicate more effectively with collaborators. This folk theory concerns using T2I models as a mechanical means of facilitating creative processes without employing T2I outputs in the final piece. Participants expressed how employing T2I models in this way facilitates or eases cumbersome aspects of their creative process, especially in early conceptual stages and to “mock-up prototypes” (see Fig 1). For instance, P6, a documentary filmmaker from Poland, described using T2I models to mood board before developing a detailed treatment (an outline of the film structure):

•	“I personally feel really empowered... to innovate in this space or be able to draft up ideas and concept sketches in my own studio without having to immediately find a collaborator to bring those ideas to life” (see Fig 2).

This folk theory of use imagines T2I models as low-stakes technology where its value is enabling creative tasks.

Figure 1:

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Figure 2:

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Figure 3:

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Another way ML-artists consider T2I models as a mundane creative tool is in using them to facilitate communication among collaborators with different expertise. Here, T2I models “work” to render ideas legible and foster shared meaning. P8, from India, described how T2I enables him to visually prototype and communicate abstract concepts (see Fig 3), elaborating:

•

“[I’m] creating a VR space [...] with another 3D artist. Sometimes [...] the ideas don’t get communicated very well and we found using Midjourney or DALL-E to give some visual form [to the ideas]…helps a lot because the ideas are quite abstract…and being able to communicate them simply in language or even poorly drawn hand-drawn figures is generally not that effective.”

In this folk theorization, T2I models enable the early stages of the creative process by facilitating iterative idea generation and communication among collaborators.

4.2 Folk Theories of Harm: T2I Models as a Target and Source of Harm

ML-artists hold distinctive folk theories of harm concerning the use of T2I models in creative practice that are in tension with common technology development practices (see Table 4). In this folk theory, ML-artists perceive T2I models as not a source of harm but something harmed by (1) engineering efforts to eliminate failures, bugs, and glitches and (2) product policy efforts to limit functionality and implement guardrails. In terms of T2I as a source of harm, they emphasize (3) harms from use are not deterministic but contextual and distributed unequally across contexts. This folk theorization is deeply entangled with how ML-artists conceive of T2I models as a creative medium and true creativity as arising from the situated motivations and social location of T2I users.

4.3 Folk Theories of Harm Reduction and Responsibility

Identifying effective harm reduction practices and ascribing responsibility for operationalizing them is an ongoing conversation in the responsible AI field. ML-artists’ folk theories about harm reduction focus on protecting T2I models, in which they articulated: (1) transparency around T2I model development will enable creative practice; and (2) increasing artist control over T2I models reduces harms to creative practice. Then, in terms of responsibility for mitigating harms, ML-artists emphasized that (3) responsibility for doing harm reduction should be distributed between the artist, model developers, moderators, and distributors of the platform (see Table 5). These folk theories are connected to how ML-artists envision T2I models as an important medium and tool for creative practice necessitating its protection, with a vision of dispersing responsibility for the creation of critical art and model governance among differently situated actors.

5.1 Folk Theories as an Argument About Sociotechnical Problems and Solutions

Folk theories reveal how situated communities experience algorithmic technologies in their lives and social relations [2, 108, 129]. HCI and folk theory scholar Michael Ann DeVito [27, p. 2] describes how folk theories (de)motivate engagement with an algorithmic system incorporate users’ “assessment of the risks and benefits” of that system or its constitutive features. As the ways differently situated communities of practice experience risks and benefits intersect with social hierarchies — such as gender, socioeconomic status, and nationality, among others — folk theories reflect the frictions between the values encoded in algorithmic systems and those held by communities [27].

Our study complements these insights to further illustrate how folk theorization can identify design frictions and function as a productive force for communities of practice to diagnose problems with ML systems and articulate proposed solutions. Interrogating how different communities frame ML problems and solutions can reveal much about how technologies reinforce or redistribute social power. Science and Technology Studies scholars Steve Woolgar and Dorothy Pawluch [125] describe how when different publics frame social problems, there is a tendency towards “ontological gerrymandering” through which certain aspects of an issue are framed as problematic or visible, while other parts remain unchallenged or invisible. These gerrymandering tendencies are evident, for example, in ML-artists’ folk theories about how T2I models are a medium with specific properties (Section 4.1.1), and thus efforts to fix glitches and limit functionality harm the medium (Section 4.2.2). Similarly, the theorization of T2I models as a tool for mundane tool for performing creative work (Section 4.1.2) relates to their understanding of harms of use as non-deterministic (Section 4.2.3) and thus increased model transparency will enable other users to understand appropriate uses (Section 4.3.1). In making decisions about which aspects of a problem should be (in)visible, theorizers express their desired (and here, self-interested) visions of technological futures.

Recommendation #1—Employ the folk theory lens to examine situated community attitudes about technology solutions and problems.. Researchers and responsible AI practitioners can employ the folk theory lens across topics of algorithmic use, harm, and harm reduction as a research method to understand how different communities articulate sociotechnical problems and solutions. HCI and responsible AI scholars advocate the importance of engaging communities to identify potential harms and harm reduction efforts [22, 31, 36, 88]. Folk theories are a kind of storytelling that animates modes of thinking, ideologies, and rationalities of the power relations encoded in technologies. For example, when asked to think about strategies for reducing harm from integrating T2I models in creative practice, ML-artists identified increasing transparency around how the model is created and expanding artist control and involvement in model development (Section 4.3).

Responsible AI researchers and practitioners can analyze folk theories as sociotechnical stories that render visible certain aspects of the problem and put these insights in service of equity-oriented design strategies. In the above example, while this folk theory of harm reduction is rooted in preserving T2I as medium, with reflexivity from practitioners (see: [22, p. 13]), analysis of folk theories may be actioned on in ways that improve technology development for a wide range of art communities, even those who are not proponents of creative ML. Thus, for practitioners, interrogating knowledge claims mobilized in folk theories can illuminate how different communities make normative claims about sociotechnical problems.

5.2 Situating Community Folk Theories in Broader Technology Discourses

Our analysis illuminates how the folk theorization of this community of practice is reciprocally shaped by their beliefs about how contextual power dynamics shape creativity. They often avoided hard framings of T2I harms; instead, they emphasized the non-deterministic nature of harm that requires discussion of context, artist intent, and viewing audiences. This approach to T2I harms intersects with two public discourses: democratizing creativity and commodifying creativity. The democratizing creativity discourse positions ML models as an unflinchingly empowering tool enabling broad access to creative pursuits [47, 85], while the commodifying creativity discourse emphasizes potential harmful outcomes of creative ML models through which the off-the-shelf ability to generate images will devalue and exploit creative labor [64, 74].

Our study illustrates how ML-artist folk theorization endorses aspects of both opposing discourses. In terms of democratizing creativity, ML-artists articulated how T2I models increase broad access to creative tools and fill gaps in tertiary education, two positive futures they envisioned. However, ML-artists also described the potential for job displacement and erosion of certain roles in creative industries, including entry-level and some technical positions.

Recommendation #2—Reflect on the distinctions between folk theories among different communities.. Our findings illuminate how T2I is reasoned as a medium, and given the partial ways ML-artists endorsed popular but opposing discourses through their folk theories of harm and use, an alternative analytical lens from media studies that reflects the multivalent impacts of technologies provides a nuanced interpretation of our results. Media scholar Marshall McLuhan [76] articulated four effects of any technology or medium on society, in which every technology retrieves prior practices in a new form; that it will often reverse into a form distinct from its original characteristics; that it simultaneously obsolesces a prior technology or practice; and that it intensifies, accelerates, or enhances some human action.

Our study suggests ML-artist folk theorization better articulates to McLuhan’s media laws, a framework previously employed to understand DIY and maker cultures [115]. Their folk theories characterize T2I models as retrieving direct representation, hegemonic social representations, and social power dynamics; as enhancing speed of creativity, the bar for “true creativity,” communication of abstract ideas, access to representation, job displacement, and an increase in the influence of the companies developing T2I models; as obsolescing certain forms of artistic labor and entry-level creative industry job roles; and in its worse form reversing into loss of creative identity, reinforcing existing power structures, and loss of artist control over their artwork.

5.3 Using Folk Theories to Inform HCI Research and Responsible AI Policy

While folk theorization is an analytical framework for understanding the informal knowledge users develop to explain the outcomes, effects, or consequences of algorithmic systems, it can inform responsible AI research and policy practitioners by inviting a human-centered approach to examine human control and algorithmic automation [106]. The ML-artist folk theories we found in this study distill findings illustrating linkages between normative understandings of T2I use, harm, and harm reduction. The field of responsible AI research and policy is a practice of developing AI systems that embody equitable outcomes. Important work in this field has focused on potential harms from ML systems, particularly computational harms arising from choices made in ML pipelines [105].

Recommendation #3—Examine folk theories across use, harm, and harm reduction to understand deeper relationships between these dimensions.. Our study illustrates the value of examining these in concert, as we found they build upon and inform each other. How ML-artists conceptualize normative uses of T2I models directly informs their views of potential harms and harm reduction strategies. Looking at these throughlines offers important insight into not only what sociotechnical harms they see but also what strategies are needed to address them. For example, ML-artists’ folk theory of how T2I models expand access to and forms of creative expression (Section 4.1.3) relates to their theorization that engineering efforts to eliminate failures, glitches, and bugs (Section 4.2.1) and limiting functionality (Section 4.2.2) harms T2I models for use in creative practice. The frictions between theories of use and harm connect to their beliefs about harm reduction, such as how “improving” T2I models involves providing artists with more knowledge about the medium and expanding the representations possible from T2I models, enabling them to control and work with the medium in new ways (Section 4.3). In this way, folk theories offer a useful analytic for practitioners to understand user knowledge.

Recommendation #4—Contextualize folk theory perspectives within the policy and regulatory landscape, with equity-oriented outcomes as a north star.. Our findings affirm how folk theories can be “in tension” with not only institutionalized conceptions of how systems work held by technology designers [37] but also ways to govern, manage, and control T2I models. ML-artist desire for full model control may be infeasible depending on a number of social, ethical, and regulatory contextual factors; however, more distributed governance could enable more equitable algorithms. Moreover, the normative desires of one community (e.g., ML-artists) may sit in tension with other communities (e.g., non-ML-artists). Future work exploring commonalities and distinctions in T2I folk theorization across communities could provide further insights for creating appropriate governance mechanisms.

5.4 Limitations

Although this study offers critical insights into ML-artist folk theorization, it has limitations. Our study examines the perspectives of artists who have or currently employ T2I models in their creative practice and thus reflects the situated knowledge of those who likely hold more favorable views toward T2I models. As such, this study may not capture the folk theorization of artists critical towards using T2I models, artists who do not have access to these models, or artists whose practice does not include digital art. While our recruitment through a research partner enabled us to convene global artists who live and work in different cultural contexts, it limited the initial pool of artists to those with direct connections to the organization or those who have relationships with someone with a direct connection.

Our study design also focused on understanding the perspectives and beliefs of one particular artist community and did not enable analysis across differently situated artist communities, such as (1) those who are unfamiliar with T2I models, (2) familiar but have not employed T2I models, and (3) those who have incorporated T2I models in their practice. Exploring the (dis)continuities of folk theories across differently situated communities and artistic intentions is a fruitful area for future research. Such work could potentially inform design or feature recommendations to develop T2I tools that better serve a wider range of artist communities.