Designing Participatory AI: Creative Professionals’ Worries and Expectations about Generative AI

Abstract

Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. Such technologies potentially introduce a massive disruption to creative fields. This paper presents the results of a qualitative survey (𝑁 = 23) investigating how creative professionals think about generative AI. The results show that the advancement of these AI models prompts important reflections on what defines creativity and how creatives imagine using AI to support their workflows. Based on these reflections, we discuss how we might design participatory AI in the domain of creative expertise with the goal of empowering creative professionals in their present and future coexistence with AI. CCS CONCEPTS• Computing methodologies → Philosophical/theoretical foundations of artificial intelligence; • Human-centered computing → Empirical studies in HCI; HCI design and evaluation methods.

Integrating generative AI in knowledge building

“…As advances in GenAI are forcing us to negotiate boundaries between human and machine intelligences especially in creative domains (Das & Varshney, 2022;Eshraghian, 2020;Inie et al, 2023), pedagogical strategies implemented in the study offers a renewed vision of education for knowledge creation. In particular, drawing on Knowledge Building's emphasis on engaging students in continual improvement of ideas (Scardamalia & Bereiter, 2022), this study shows the potential of leveraging GenAI to further augment students' creativity and epistemic agency.…”

PlantoGraphy: Incorporating Iterative Design Process into Generative Artificial Intelligence for Landscape Rendering

“…While the remarkable efficiency and generation ability have been shown in technique manner, some limitations impede the usability of GAI in practical way of design. The first and foremost reason is that, most diffusion models are realized in an end-to-end manner with input text prompts and output images, which overlook the "exploration" stage as one of the most important parts in creative process [21]. In addition, the lack of controllability cannot support stylistic consistency outputs, which is crucial for iterative design in practical scenarios [43].…”

Generative Artificial Intelligence in Applied Business Contexts: A Systematic Review, Lexical Analysis, and Research Framework

“…Drawn from the complete dataset, the applied business dataset (GenAI-AB) is comprised of twenty articles. The articles are focused on applied business contexts and disciplines including general business (Beerbaum, 2023b;Brynjolfsson et al, 2023;Eisfeldt et al, 2023;Houde et al, 2020;Inie et al, 2023;Korzynski et al, 2023;Zhong et al, 2023;Zq et al, 2020), management (Bilgram & Laarmann, 2023;Kanitz et al, 2023;Korzynski et al, 2023;Lim et al, 2023), marketing (Mayahi & Vidrih, 2022), finance (Krause, 2023b(Krause, , 2023a(Krause, , 2023c, accounting (Beerbaum, 2023a), and information systems (Euchner, 2023;Ferrag et al, 2023;Mollick & Euchner, 2023).…”

“…Only five of the GenAI-AB articles are applied in nature and thus comprise the applied business empirical research (GenAI-AB-Emp) dataset (Brynjolfsson et al, 2023;Eisfeldt et al, 2023;Inie et al, 2023;Zhong et al, 2023;Zq et al, 2020). The AB-Emp dataset offers a valuable lens into research applied business disciplines may choose to explore and extend.…”