From issues of intellectual property rights, environmental impact,and the question of creativity in itself, Creative AI  (AI applied to arts and design) presents many pressing ethical challenges. At thes ame time, AI ethics guidelines have been criticised for their rationalist inactionability, highlighting the need for finding ground-up approaches for exploring situated ethics and anticipatory – rather than reactive – ethics in AI technology design. In this paper, we present the results of organising two workshops with Creative AI practitioners in writing fictional research abstracts (FRAs). The participants wrote future-oriented research scenarios for Creative AI and engaged in discussion about them, with the aim to critically reflect on Creative AI ethics and futures in an anticipatory manner. In this paper, we provide an analysis of the imaginaries within the abstracts (e.g. stakeholders, technology framing, and scientific study framing in the abstract), as well as a thematic analysis of topics evoked in and by the abstracts. We observe that the FRAs facilitated five different kinds of thematic discussions, of which nature of creativity and role on AI in the society were particularly prevalent. We also witnessed socio-technical continuity that brought current and past conditions as constraints into the future scenarios. We subsequently reflect on how the FRAs engaged with ethical questions using the Anticipatory Technology Ethics perspectives of technology, artefact, and application. Our work contributes to the empirical understanding of the ethical concerns of future Creative AI technologies and their role in society, while extending the empirical insights of applying the FRA method into a situated case of anticipatory ethics reflection.

The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology, matters of fact and concern and feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine the implicit, the wider connections and entangled filaments in Human–AI musical interactions.

Reenactment forms a unique method of exploring the social, political, historical, conceptual, contextual and other aspects of electronic sound instruments from the past, without necessarily reproducing the instrument’s physical, functional or sonic char- acteristics. Rather, the reenactment presents a novel instrument, realized through contemporary means, reflecting on contempo- rary concerns and within a contemporary context. We find reen- actment complementary to conservation, maintenance, recon- struction and emulation in working with archival and museum objects. Our paper presents an analytic framework developed for use in workshop scenarios. The series of questions within this framework helps determine and understand which aspects of an instrument might be reenacted. To illustrate the process in ac- tion, we describe an example workshop wherein participants use methods of media archaeology, design fiction and role playing to imagine and reenact new features, affordances, contexts and applications of electronic instruments from a museum exhibition. 

The design justice of AI technologies is infuenced by value tensions, by which values get embedded and excluded in design processes. In this study, we investigate Euro-Western artists’ expressed values for the design of Creative AI technologies. We conducted four workshops, in which artists engaged in value sketching and critical discussion around Creative AI, and analyzed emerging values. In the sketches, the artists predominantly expressed values of self-interest, while when encouraged for a critical discussion artists expressed values aligned with interest of others. We discuss the challenge of aligning Creative AI with design justice, when values of common good are not inherently part of the technological imaginaries, but rather need to be explicitly evoked. We open up these value tensions, discussing values’ actionability, and the implications for design futures of Creative AI. With this paper, we extend the empirical studies in value sketching and socio-technical landscape of Creative AI.

As music generated using artificial intelligence (AI music) becomes more prevalent — originating not only from individuals but also commercial services — the need to study it and its impacts becomes important. How can this material and its sources be meaningfully studied and critically engaged with, especially considering the unprecedented scales possible with generative AI? The paper begins to answer this question by considering AI music along seven aspects: 1) the company providing an AI music service; 2) its founders and employees; 3) the use of the service; 4) the users; 5) the algorithms; 6) the music; and 7) the sustainability. We make our discussion more concrete by considering the contemporary AI music service Boomy. While our investigations are preliminary and focused on a single AI music service, we argue that they open several interesting avenues of exploration for many disciplines and their intersections to help prepare for the coming flood of AI music. This paper asks many more questions than it answers, which is a feature (not a bug) of it advocating for a new domain of study: AI Music Studies.

Feverish narratives of artistic AI-revolution obscure the fact that empirical documentation of the actual impacts of artificial intelligence on artistic practices is still sparse. This paper focuses on the frictions of working with AI artistically. Based on interviews with 20 AI-artists, we 1) demonstrate that frictions experienced with the technological elements of the work processes with AI are inseparably intertwined with the artists’ socio-material realities and the inherent asymmetries of access, and 2) show how frictional ambivalence and unpredictability in artistic interactions with AI tools function both as restrictive and productive elements of the art-making processes, presenting opportunities to reframe the core notions of artistic agency, authorship, and the ontology of art.We discuss these findings in the context of HCI and critical data studies and provide three invitations for designing with and for frictions. Our empirical work contributes to a deeper understanding of the emerging community of AI-artists and invites new mindful perspectives for the design and development of Creative AI applications.

The environmental footprint of Generative AI and other Deep Learning (DL) technologies is increasing. To understand the scale of the problem and to identify solutions for avoiding excessive energy use in DL research at communities such as ISMIR, more knowledge is needed of the current energy cost of the undertaken research. In this paper, we provide a scoping inquiry of how the ISMIR research concerning automatic music generation (AMG) and computing-heavy music analysis currently discloses information related to environmental impact. We present a study based on two corpora that document 1) ISMIR papers published in the years 2017–2023 that introduce an AMG model, and 2) ISMIR papers from the years 2022–2023 that propose music analysis models and include heavy computations with GPUs. Our study demonstrates a lack of transparency in model training documentation. It provides the first estimates of energy consumption related to model training at ISMIR, as a baseline for making more systematic estimates about the energy footprint of the ISMIR conference in relation to other machine learning events. Furthermore, we map the geographical distribution of generative model contributions and discuss the corporate role in the funding and model choices in this body of work.

Artistic uses of AI technologies are fast gaining prominence in a number of creative domains. In this paper, we describe our preliminary research exploring the challenges and changes that working with AI poses to artists based on interviews with N=20 artists. We present preliminary themes relating to challenges and changes that artists are encountering and highlight the importance of studying AI further in situated artistic practices.

This study presents a sonification tool – SonifyFOLK –designed for intuitive access by musicians and dancers in their sonic explorations of movements in dance performances. It is implemented as a web-based application to facilitate accessible audio parameter mapping of movement data for non-experts, and applied and evaluated with Swedish folk musicians and dancers in their exploration of sonifying dance. SonifyFOLK is based on the WebAudioXML Sonification Toolkit and is designed within a group of artists and engineers using artistic goals as drivers for the sound design. The design addresses challenges of providing an accessible interface for mapping movement data to audio parameters, managing multi-dimensional data and creating audio mapping templates for a contextually grounded sound design. The evaluation documents a diversity of sonification outcomes, reflections by participants that imply curiosity for further work on sonification, as well as the importance of the immediacy of the both visual and acoustic feedback of parameter choices.