The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most conceive (...) of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

While discussions of the imagination have been limited in philosophy of science, this is beginning to change. In recent years, a vast literature on imagination in science has emerged. This paper surveys the current field, including the changing attitudes towards the scientific imagination, the fiction view of models, how the imagination can lead to knowledge and understanding, and the value of different types of imagination. It ends with a discussion of the gaps in the current literature, indicating avenues for future (...) research. (shrink)

This article starts an engagement on the aesthetics of experiments and offers an account for analysing how aesthetics features in the design, evaluation and reception of experiments. I identify two dimensions of aesthetic evaluation of experiments: design and significance. When it comes to design, a number of qualities, such as simplicity, economy and aptness, are analysed and illustrated with the famous Meselson-Stahl experiment. Beautiful experiments are also regarded to make significant discoveries, but I argue against a narrow construal of experimental (...) aims. By drawing on the plurality of goals experimenters have and diversity of aesthetic responses, I argue that experiments are aesthetically appreciated both when they discover and when they produce disruptive results. (shrink)

This article examines the artistic potential of forms of image-making that involve registering the features of real objects using mind-independent processes. According to skeptics, these processes limit an agent’s intentional control over the features of the resultant “automatic images,” which in turn limits the artistic potential of the work, and the form as a whole. I argue that this is true only if intentional control is understood to mean that an agent produces the features of the work by their own (...) bodily movements alone. Not only is this an unrealistic standard to uphold, but I show that a definition of intentional control based on the skeptic’s position does not prohibit an agent from realizing the features of an image by means beyond their own actions. An agent can exercise intentional control over the features of an image if they successfully anticipate the effect that the remote consequences of their actions will have on these. This, I argue, entails that to exert intentional control over the features of an image is to exercise “creative agency,” which is a species of executive agency. Consequently, I defend the idea that the origin of automatic images in creative agency grounds their artistic significance. (shrink)

Increasingly, artists describe the feeling of creating images with generative AI systems as like working with a “collaborator”—a term that is also common in the scholarly literature on AI image-generation. If it is appropriate to describe these dynamics in terms of collaboration, as I demonstrate, it is important to determine the form and nature of these joint efforts, given the appreciative relevance of different types of contribution to the production of an artwork. Accordingly, I examine three kinds of collaboration that (...) can be found in the philosophical literature on artistic authorship—collective authorship, co-creatorship, and co-production—to determine whether human-AI interactions comprise joint efforts as per such kinds. As I find, collaboration is a concept that invokes rich psychological terms and so one to used be with care in relation to generative AI, which does not yet meet the conditions to count as an artistic collaborator in the senses derived from the literature. To progress discussions on ethical and legal issues that are raised by image-making practices involving generative AI, and further research into the distinctive qualities afforded by interactions with these systems, I argue that we ought to frame their contributions to the production of visual artworks in terms of a “generative” contribution and describe the interactions between humans and generative AI systems as “AI-assisted production”. (shrink)

1. In the last decade, technological systems based on Artificial Intelligence (AI) architectures entered our lives at an increasingly fast pace. Virtual assistants facilitate our daily tasks, recom...