In recent years, advances in artificial intelligence (AI) and machine learning have given rise to powerful new tools and methods for creative practitioners. 2022–2023 in particular saw an explosion in generative AI tools, models and use cases. Noting the long history of critical arts engaging with AI, this chapter considers both the application of generative AI in the creative industries, and ways in which artists co-shape the development of these emerging technologies. After reviewing the landscape of generative AI in visual arts, music and games, we propose four areas of critical interest for the future co-shaping of generative AI and creative practice in the areas of communities and open source, deeper engagement with AI, beyond the human and cultural feedbacks.

This paper presents the design, deployment and qualitative study of a large-scale, public, generative art exhibition, through which passers-by could create artworks, and mint a non-fungible-token (NFT). Following the month-long exhibition, during which 229 anonymous participants produced artworks, 69 non-transferable NFTs were minted, we surveyed (33) and interviewed (14) expert and novice participants about their experiences. We explored contemporary challenges of owning digital things, and the extent to which NFTs, and 'Web3' technologies offer meaningful forms of ownership. Our findings describe how the inability to trade this NFT, and its unique circumstances of acquisition, made it meaningful in ways that extended beyond its immediate (limited) utility and offered participants something through which to construct identity. Reflecting on the aspirations, contradictions, and misconceptions of forms of ownership enabled by NFTs, we conclude with proposals for renewed attention in HCI to the nature of digital possessions, and the potential for 'ownership design'.

This work illustrates how artistic robotic systems can provide a reservoir of unfamiliarity and a basis for speculation, to open the field toward new ways of thinking about HRI. We reflect on a collaborative project between design students, a media art studio, and design researchers working with the baggage handling department of the Schiphol airport. Engaging with the industrial context, we developed 'metabehaviours' - abstracted ideas of processes carried out on the worksite-and passed these over to the students who translated them into robotic enactions using a predefined hardware developed by the media art studio. The resulting visit experience challenges the audience to decode the installation in terms of metabehaviours and their possible relations to industrial HRI. We used this to reflect on the value of conducting artistic and speculative work in HRI and to distil actionable recommendations for future research.

There is a call for more use of future-oriented design methods like speculative de-sign in developing policies. While these methods offer potential benefits in helping future-proof policies, they also run the risk of solidifying existing structures of pow-er if not applied critically. In this paper, we describe a case study examining smart doorbells in Amsterdam, where we created a speculative design exhibition grounded in feminist theory in order to challenge the existing power structures in the public domain. We then discuss the insights from our design process and the reaction the exhibition received in light of how feminist theory can help ensure a critical application of future-oriented design methods in policy design.

This article aims to develop a semi-formal representation for Human-AI (HAI) interactions, by building a set of interaction primitives which can specify the information exchanges between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can capture common interactions between humans and AI/ML models. The motivation behind this is twofold: firstly, to provide a compact generalization of existing practices for the design and implementation of HAI interactions; and secondly, to support the creation of new interactions by extending the design space of HAI interactions. Taking into consideration frameworks, guidelines, and taxonomies related to human-centered design and implementation of AI systems, we define a vocabulary for describing information exchanges based on the model’s characteristics and interactional capabilities. Based on this vocabulary, a message passing model for interactions between humans and models is presented, which we demonstrate can account for existing HAI interaction systems and approaches. Finally, we build this into design patterns which can describe common interactions between users and models, and we discuss how this approach can be used toward a design space for HAI interactions that creates new possibilities for designs as well as keeping track of implementation issues and concerns.

Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability, and methodology to support, deepen, and extend those contributions.

This paper examines the role that enchantment plays in the design of AI things by constructing a taxonomy of design approaches that increase or decrease the perception of magic and enchantment. We start from the design discourse surrounding recent developments in AI technologies, highlighting specific interaction qualities such as algorithmic uncertainties and errors and articulating relations to the rhetoric of magic and supernatural thinking. Through analyzing and reflecting upon 52 students’ design projects from two editions of a Masters course in design and AI, we identify seven design principles and unpack the effects of each in terms of enchantment and disenchantment. We conclude by articulating ways in which this taxonomy can be approached and appropriated by design/HCI practitioners, especially to support exploration and reflexivity.

While the past decade has seen cuts to public funding to the arts, it has also seen the development of online technologies which have the potential to reach increasingly diverse and global audiences. As a result, individuals and organisations across the creative industries and performing arts have experimented and embraced more diverse, innovative, and direct approaches to engage and monetise tangible support from their audiences and communities. Prior work has identified the evolution of crowdfunding in the arts as a form of ‘crowd patronage’ – where platforms such as Patreon and Kickstarter function as new intermediaries that can radically reconfigure how and why creative work is funded. The ‘pivot to digital’ – which brought audiences and creative workers together in new online spaces throughout the pandemic – further reinforced the potential for direct communication and financial support from audiences of creative work. This chapter will reflect on how contemporary data-driven, monetary technologies have begun to decentralise how creative work is valued, supported, and paid for, with a particular focus on the performing arts.

In Industry 5.0, cognitive robots and workers will engage in evolving and reciprocal relations, which we call worker-robot relationships (WRRs). To enable evidence-based work futures with workers, we must co-develop WRRs and understand their impact on work, workers, management, and society. To this end, we posit that the HRI field should work beyond disciplines and include value-driven and plural perspectives through transdisciplinary research done with and for workers. However, WRRs and transdisciplinarity pose unique technical, design, and methodological challenges yet to be explored. We propose a workshop to engage the HRI community working on Industry 5.0, aiming at 1) taking stock of current WRR-related challenges in relevant disciplines, 2) collectively kick-off the exploration of a joint research agenda, 3) preliminary examining if and how transdisciplinarity could help the HRI community, and 4) start discussing how to deal with such complex knowledge integration in practice.

This study investigates error handling intricacies in supervised autonomy orbit-to-ground teleoperation for space exploration robots, emphasizing scenarios with communication delays that render Earth-based ground control assistance unfeasible. In this setting, one major challenge lies in empowering the crew to independently mitigate robot errors that may occur as the robot plans its actions. To address this limitation of current supervised autonomy interfaces, we propose a third-person perspective and game design principles to improve environmental awareness in error situations. 16 experts with similar technical background as the target crew members tested the interface in a physical user study, while 42 people assessed it in an online study. We conclude that a third-person view brings significant improvements to mental workload, overall experience and the ability to identify and rectify planning errors.

Seen both as a resource and an obstacle to clarity, uncertainty is a concept that permeates many areas of design. As the concept gains prominence in Human-Computer Interaction (HCI), this special issue specifically explores the interplay between uncertainty and prototyping in Research through Design (RtD). We first outline three histories of uncertainty in design, in relation to its philosophical significance, its role in statistical and algorithmic processes, and its importance in prototyping. The convergence of these aspects is crucial as design evolves toward more agentive and entangled systems, introducing challenges such as Design as a Probabilistic Outcome. We then investigate the design spaces for engaging with “being uncertain” that emerge from the papers: from nuancing the relationship between designers and quantitative data to blurring the line between humans, fungi, and algorithms. Finally, we illuminate some preliminary threads for how RtD can navigate and engage with these shifting technological and design landscapes thoughtfully.

In this case study, we document the process of engaging in an initially unplanned and informal knowledge exchange activity between academic researchers and a local performing arts company. This knowledge exchange activity quickly became a fruitful collaboration during which an academic design research prototype was reimagined as a wholly new product to expand the offering of a creative business. We document the factors that led to the success of this collaboration, reflecting on both features of the collaboration itself and how the design of the initial research prototype configured its repurposing. In terms of the latter, we consider how the original prototype's ambiguity, open-endedness, customisability and flexible assembly afforded its reimagining. Through the case study, we demonstrate that there is much to be gained from facilitating access to research prototypes for small and medium enterprises and supporting them in appropriating these toward their own goals.

Ethics should be a practice, not a checkbox. Data scientists want to answer questions about individuals and society using the vast torrent of data that flows around us. Machine learning practitioners want to develop and connect complex
models of the world and use them safely in critical situations. Ethical issues can be seen as getting in the way of the core idea and form pain points around managing, using and learning from data, as well as designing human-centric and ethical systems. This is because there is a design gap around ethics in data
science and machine learning: the tools that we use do not support ethical data use, which means that data scientists and machine learning practitioners, already engaged in technically complex, multidisciplinary work, must add another dimension to their thinking. This work proposes and outlines an infrastructure and framework that can support in-the-moment ethical decision
making and recording, as well as post-hoc audits and ethical model deployment.

Basic human rights, like freedom of expression, freedom of the press, and privacy, are being radically transformed by new technologies. The manifestation of these rights in online spaces is known as “digital rights,” which can be impeded or empowered through the design, governance, and litigation of emerging technologies. Design defines how people encounter the digital world. Some design choices can exploit the right to privacy by commodifying attention through tactics that keep users addicted to maximize profitability; similar design mechanisms and vulnerabilities have facilitated the abuse of journalists and human rights advocates across the globe. But design can also empower human rights, providing novel tools of resistance, accountability, and accessibility, as well as the inclusion of previously underserved voices in the development process. The new capabilities offered by these technologies often transcend political boundaries, presenting complex challenges for meaningful governance and regulation. To address these challenges, collaborations like the Internet Governance Forum and NETmundial have brought together stakeholders from governments, nonprofits, industry, and academia, with efforts to address digital rights like universal internet access. Concurrently, economic forces and international trade negotiations can have substantial impacts on digital rights, with attempts to enforce steeper restrictions on intellectual property. Private actors have also fought to ensure their digital rights through litigation. In Europe, landmark cases have reshaped the international management of data and privacy. In India, indefinite shutdowns of the internet by the government were found to be unconstitutional, establishing online accessibility as a fundamental human right, intimately tied with the right to assembly. And in Africa, litigation has helped ensure freedom of speech and of the press, rights that may affect more individualsas digital technologies continue to shape media. These three spheres—design, diplomacy, and law—illustrate the complexity and ongoing debate to define, protect, and communicate digital rights.

When creating conversational agents, designers have to make decisions about the way the agents present themselves. In this position paper, we identify and synthesize ethical dilemmas that conversational interface designers and researchers face around gender of conversational agents. First, we identify three layers that cause tension in designing conversational agents’ gender: (i) interactional qualities; (ii) goal-orientation; and (iii) societal issues. We then argue that conversational agent designers and re- searchers can navigate this problem space by comparing two ethical frameworks: a utilitarian perspective and a dialogical ethics perspective. Finally, we argue that dialogical ethics can be a balanced, ethical lens that can help conversational agent designers and researchers make design decisions about the gender of an agent

Recent research claims that information cues and system attributes of algorithmic decision-making processes affect decision subjects' fairness perceptions. However, little is still known about how these factors interact. This paper presents a user study (N = 267) investigating the individual and combined effects of explanations, human oversight, and contestability on informational and procedural fairness perceptions for high- and low-stakes decisions in a loan approval scenario. We find that explanations and contestability contribute to informational and procedural fairness perceptions, respectively, but we find no evidence for an effect of human oversight. Our results further show that both informational and procedural fairness perceptions contribute positively to overall fairness perceptions but we do not find an interaction effect between them. A qualitative analysis exposes tensions between information overload and understanding, human involvement and timely decision-making, and accounting for personal circumstances while maintaining procedural consistency. Our results have important design implications for algorithmic decision-making processes that meet decision subjects' standards of justice.

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course (n = 100) nine ‘AI exercises’ that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.

Research in the wild can reveal human behaviors, contexts, and needs around products that are difficult to observe in the lab. Telemetry data from the use of physical products can help facilitate in the wild research, in particular by suggesting hypotheses that can be explored through machine learning models. This paper explores ways for designers without strong data skills to engage with multimodal data to develop a contextual understanding of product use. This study is framed around a lightweight version of a data enhanced design research process where multimodal telemetry data was captured by a GoPro camera attached to a bicycle. This was combined with the video data and conversation with the rider to carry out an exploratory sensemaking process and generate design research questions that could potentially be addressed through data capture, annotation, and machine learning. We identify a range of ways that designers could make use of the data for ideation and developing context through annotating and exploring the data. Participants used data and annotation practices to connect the micro and macro, spot interesting moments, and frame questions around an unfamiliar problem. The work follows the designers’ questions, methods, and explorations, both immediate concerns and speculations about working at larger scales with machine learning models. This points to the possibility of tools that help designers to engage with machine learning, not just for optimization and refinement, but for creative ideation in the early stages of design processes.

This paper presents an annotated portfolio of projects that seek to understand and communicate the social and societal implications of blockchains, DLTs and smart contracts. These complex technologies rely on human and technical factors to deliver cryptocurrencies, shared computation and trustless protocols but have a secondary benefit in providing a moment to re-think many aspects of society, and imagine alternative possibilities. The projects use design and HCI methods to relate blockchains to a range of topics, including global supply chains, delivery infrastructure, smart grids, volunteering and charitable giving, through engaging publics, exploring ideas and speculating on possible futures. Based on an extensive annotated portfolio we draw out learning for the design of blockchain systems, broadening participation and surfacing questions around imaginaries, social implications and engagement with new technology. This paints a comprehensive picture of how HCI and design can shape understandings of the future of complex technologies

In this paper, we discuss the trends and challenges of the integration of Artificial Intelligence (AI) methods in the workplace. An important aspect towards creating positive AI futures in the workplace is the design of fair, reliable and trustworthy AI systems which aim to augment human performance and perception, instead of replacing them by acting in an automatic and non-transparent way. Research in Human-AI Interaction has proposed frameworks and guidelines to design transparent and trustworthy human-AI interactions. Considering such frameworks, we discuss the potential benefits of applying human-in-the-loop (HITL) and explainable AI (XAI) methods to define a new design space for the future of work. We illustrate how such methods can create new interactions and dynamics between human users and AI in future work practices.