The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability through creative and artistic domains. The previous workshops explored the landscape and the speculative futures of AI in creative processes. To respond to emerging challenges and contribute to creative and societal transformation more broadly, this workshop focuses on the operationalisation of XAI in the Arts. Specifically, we will: i) critically reflect on emerging practices that encourage diversity and inclusivity in XAI; ii) collectively ideate a library of missing projects to encourage future collaborations and speculations; iii) scope the development of a resource hub for open XAIxArts projects to archive tangible XAI interventions and facilitate future community building with the wider discourse on Human-Centred AI.

Control is a critical yet underexplored concept in human-AI co-creativity and more broadly human-AI collaboration, where AI systems are expected to act as collaborative partners with creative autonomy. Existing frameworks for characterizing control remain limited and often fall short in capturing the tensions and complexities of co-creation dynamics. In this paper, we examine how experts conceptualize control and expect human-AI control dynamics by leveraging a recent framework on characterizing control as our theoretical probe. We conduct a semi-structured focus group study with nine experts in HCI, co-creativity, and AI. Our findings reveal that control is widely viewed as a dynamic, context-dependent construct that should adapt across different phases of co-creation, domains, and levels of trust in AI. Drawing on our findings, we propose a conceptualization of control along with actionable design implications for designing such AI systems. This work contributes to the literature on Human-AI collaboration, Computational Creativity, and HCI, advancing our understanding of control in co-creative human-AI partnerships.

The third workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts.XAI is a key concern of Responsible and Human-Centred AI, emphasising the use of HCI techniques to explore how to make complicated and opaque AI models more understandable to people.The previous workshops moved from mapping the landscape of XAI for the Arts to co-developing an XAIxArts manifesto.To continue driving discourse on XAIxArts, the anticipated outcomes of this workshop are: i) fresh insights into the evolving challenges of AI bias, lack of transparency and barriers to inclusivity through discussion of current and emerging XAIxArts practices; ii) co-developed speculative futures which expand XAIxArts discourse beyond post-hoc rationalisations of AI decisions into the imaginative possibilities of AI as an interlocutor in the creative process; iii) plans for a co-developed proposal of an edited book on XAIxArts; and iv) community expansion and engagement in wider discourses on Responsible and Human-Centred AI.

Effective communication between AI and humans is essential for successful human-AI co-creation.However, many current co-creative AI systems lack effective communication, which limits their potential for collaboration.This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI, developed through a systematic review of 107 full-length papers.FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication.To improve the framework, we conducted a preliminary study with two focus groups involving skilled individuals in AI, HCI, and design.These sessions sought to understand participants' preferences for AI communication, gather their perceptions of the framework, collect feedback for refinement, and explore its use in co-creative domains like collaborative writing and design.Our findings reveal a preference for a human-AI feedback loop over linear communication and emphasize the importance of context in fostering mutual understanding.Based on these insights, we propose actionable strategies for applying FAICO in practice and future directions, marking the first step toward developing comprehensive guidelines for designing effective human-centered AI communication in co-creation.

As AI becomes increasingly prevalent in creative domains, it is imperative to understand users’ mental models of AI in human–AI co-creation as mental models shape user experiences. Additionally, gaining insights into users’ mental models is essential for the development of human-centered co-creative AI. This article introduces a framework for exploring users’ mental models of co-creative AI. Using a large-scale study (n = 155), we explore mental models of two existing AI systems, ChatGPT and Stable Diffusion, in co-creation contexts. Participants engaged in creative tasks with both AI and completed surveys, revealing insights into mental models and their associations with demographic factors and users’ ethical stances. The results highlight the major types and patterns of mental models of AI in co-creative contexts. Findings also reveal that individuals with expertise in AI typically have Partnership-oriented mental models of co-creative AI, while those lacking AI literacy tend to have more Tool-oriented mental models. Furthermore, individuals with Partnership-oriented mental models usually have a positive ethical perspective toward anthropomorphism in AI, data collection by AI, and AI’s societal impact. Additionally, results highlight that conversational co-creative AI is generally perceived as a collaborator, whereas non-conversational AI is typically viewed as a tool.

This second workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how to make complex and difficult to understand AI models more understandable to people. Our first workshop explored the landscape of XAIxArts and identified emergent themes. To move the discourse on XAIxArts forward and to contribute to Human-Centred AI more broadly this workshop will: i) bring researchers together to expand the XAIxArts community; ii) collect and critically reflect on current and emerging XAIxArts practice; iii) co-develop a manifesto for XAIxArts; iv) co-develop a proposal for an edited book on XAIxArts; v) engage with the wider discourse on Human-Centred AI.

This first workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how complex and difficult to understand AI models such as deep learning techniques can be made more understandable to people. However, XAI research has primarily focused on work-oriented and task-oriented explanations of AI and there has been little research on XAI for creative domains such as the Arts. This workshop will: i) build an XAIxArts research community; ii) map out the current and future possible landscapes of XAIxArts; iii) critically reflect on the potential of XAI for the Arts, forming the basis for an edited book on XAIxArts and an international network of researchers.

In a human-AI co-creation, AI not only categorizes, evaluates and interprets data but also generates new content and interacts with humans. As co-creative AI is a form of intelligent technology that directly involves humans, it is critical to anticipate and address ethical issues during all design stages. The open-ended nature of human-AI interactions in co-creation poses many challenges for designing ethical co-creative AI systems. Researchers have been exploring ethical issues associated with autonomous AI in recent years, but ethics in human-AI co-creativity is a relatively new research area. In order to design human-centered ethical AI, it is important to understand the perspectives, expectations, and ethical concerns of potential users. In this paper, we present a study with 18 participants to explore several ethical dilemmas and challenges in human-AI co-creation from the perspective of potential users using a design fiction (DF) methodology. DF is a speculative research method that depicts a new concept or technology through stories as an intangible prototype. We present the findings from the study as potential users’ perspectives, stances, and expectations around ethical challenges in human-AI co-creativity as a basis for designing human-centered ethical AI partners for human-AI co-creation.

Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This article focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In a creative collaboration, communication is an essential component among collaborators. In many existing co-creative systems, users can communicate with the AI, usually using buttons or sliders. Typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners rather than just a tool. This paper presents a study with 38 participants to explore the impact of two interaction designs, with and without AI-to-human communication, on user engagement, collaborative experience and user perception of a co-creative AI. The study involves user interaction with two prototypes of a co-creative system that contributes sketches as design inspirations during a design task. The results show improved collaborative experience and user engagement with the system incorporating AI-to-human communication. Users perceive co-creative AI as more reliable, personal, and intelligent when the AI communicates to users. The findings can be used to design effective co-creative systems, and the insights can be transferred to other fields involving human-AI interaction and collaboration.

The creative sketching partner (CSP) is a proof of concept intelligent interface to inspire designers while sketching in response to a specified design task. With this interactive system we are studying the effect of an AI model of visual and conceptual similarity for selecting the Al's sketch response as an inspiration to the current state of the user's sketch. Specifically, we are interested in the user's behavior and response to an AI partner when engaged in a design task. By developing deep learning models of the sketches from a large-scale dataset, the user can control the amount of visual and conceptual similarity of the AI response when requesting inspiration from the CSP. We conducted a study with 50 design students to examine the participants' interaction behavior and their self reports. The participants' behavior maps into clusters that are co-related with three types of design creativity: combinatorial, exploratory, and transformational. Our findings demonstrate that the tool can facilitate ideation and overcome design fixation. In addition, analysis suggests that inspiration related to conceptual similarity is more associated with transformational creativity and inspiration related to visual similarity occurs more frequently during the detailed stages of design and is more prevalent with combinatorial creativity.