Filtered by themeHuman-AI co-creation9 papersclear ✕

Granting Non-AI Experts Creative Control Over AI SystemsMIchelle S. Lam · 2024Many harmful behaviors and problematic deployments of AI stem from the fact that AI experts are not experts in the vast array of settings where AI is applied. Non-AI experts from these domains hold promising potential to contribute their expertise and directly design the AI systems that impact them, but they face substantial technical and efort barriers. Could we redesign AI development tools to match the language of non-technical end users? My re­ search develops novel systems allowing non-AI experts to defne AI behavior in terms of interpretable, self-defned concepts. Mono­ lithic, black-box models do not yield such control, so we introduce techniques for users to create many narrow, personalized models that they can better understand and steer. We demonstrate the success of this approach across the AI lifecycle: from designing AI objectives to evaluating AI behavior to authoring end-to-end AI systems. When non-AI experts design AI from start to fnish, they notice gaps and build solutions that AI experts could not—such as creating new feed ranking models to mitigate partisan animosity, surfacing underreported issues with content moderation models, and activating unique pockets of LLM behavior to amplify their personal writing style. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). UIST Adjunct ’24, October 13–16, 2024, Pittsburgh, PA, USA © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0718-6/24/10 [https://doi.org/10.1145/3672539.3686714](https://doi.org/10.1145/3672539.3686714)

UX of AI & PersonalizationResearch Papers

Shaping Human-AI Collaboration: Varied Scafolding Levels in Co-writing with Language ModelsSomayeh Molaei · 2024Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how vary­ ing levels of scafolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects feld experi­ ment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence sug­ gestions (low scafolding), and next-paragraph suggestions (high scafolding). Our fndings reveal a U-shaped impact of scafold­ ing on writing quality and productivity (words/time). While low scafolding did not signifcantly improve writing quality or productivity, high scafolding led to signifcant improvements, es­ pecially benefting non-regular writers and less tech-savvy users. No signifcant cognitive burden was observed while using the scaf­ folded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scafolding mechanisms. CCS CONCEPTS • Human-centered computing → Collaborative and social computing design and evaluation methods; Empirical studies in collaborative and social computing.

UX of AI & PersonalizationResearch Papers

Farsighted-Fostering Responsable AI awarness during AI application prototypingWang · 2024Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during ∗The work was done when the authors were at Google Research. This work is licensed under a Creative Commons Attribution 4.0 International License. CHI ’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0330-0/24/05. [https://doi.org/10.1145/3613904.3642335](https://doi.org/10.1145/3613904.3642335) prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user’s prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co- design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on 1 arXiv:2402.15350v2 [cs.HC] 2 Jul 2024 CHI ’24, May 11–16, 2024, Honolulu, HI, USA Zijie J. Wang, et al. their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: [https://pair-code.github.io/farsight](https://pair-code.github.io/farsight). CCS CONCEPTS • Human-centered computing →Interactive systems and tools; • Computing methodologies →Machine learning.

UX of AI & PersonalizationResearch Papers

Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature ReviewYang Shi, Tian Gao et al. · 2023Recent interest in design through the artificial intelligence (AI) lens is rapidly increasing. Designers, as a special user group interacting with AI, have received more attention in the Human-Computer Interaction community. Prior work has discussed emerging challenges that persist in designing for AI. However, few systematic reviews focus on AI for design to understand how designers and AI can augment each other's complementary strengths in design collaboration. In this work, we conducted a landscape analysis of AI for design, via a systematic literature review of 93 papers. The analysis first provides a bird's eye view of overall patterns in this area. The analysis also reveals three themes interpreted from the paper corpus associated with AI for design, including AI assisting designers, designers assisting AI, and characterizing designer-AI collaboration. We discuss the implications of our findings and suggested methodological proposals to guide HCI toward research and practices that center on collaborative creativity.

UX of AI & PersonalizationResearch Papers

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data WorkJen Rogers · 2023Automated Machine Learning (AutoML) technology can lower bar- riers in data work yet still requires human intervention to be func- tional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures Au- toML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoML Trace a visual interactive sketch showing both the con- text and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage sce- nario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work. Availability of Supplemental Materials: https://osf.io/3nmyj/ ?view_only=19962103d58b45d289b5c83421f48b36 This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’23, April 23–28, 2023, Hamburg, Germany © 2023 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9421-5/23/04. https://doi.org/10.1145/3544548.3580819 CCS CONCEPTS • Human-centered computing →Visualization theory, concepts and paradigms; • Computing methodologies →Artificial intelli- gence.

UX of AI & PersonalizationResearch Papers

End-User Development for Artificial Intelligence: A Systematic Literature ReviewAndrea Esposito · 2023In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.

UX of AI & PersonalizationResearch Papers

Human-Machine Co-Creation in the Rise of AIWai Lok Woo · 2020A note from Melanie Ooi: In this issue, Wai Lok Woo shares with us some insights into how we will evolve with the rise of artificial intelligence. It is an extremely engaging column that applies to almost all of our work in instrumentation and measurement today. I hope you will enjoy reading this as much as I did.

UX of AI & PersonalizationResearch Papers

Identifying the Intersections: User Experience + Research Scientist Collaboration in a Generative Machine Learning InterfaceClaire Kayacik, Sherol Chen et al. · 2019Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three months to provide a human-centered perspective on the creation of ML models. The first full-time cohort of UXers were embedded in a team of ML research scientists focused on deep generative models to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faced during execution, and insights gained as a result of the process. We offer practical suggestions Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). CHI’19 Extended Abstracts, May 4–9, 2019, Glasgow, Scotland UK © 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5971-9/19/05. https://doi.org/10.1145/3290607.3299059 CHI 2019 Case Study CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK CS09, Page 1 for how to foster communication between UX and ML research teams and recommended UX design processes for building creative generative machine learning interfaces. CCS CONCEPTS • Human-centered computing; • Human computer interaction; • Interaction paradigms; • Collaborative interaction;

UX of AI & PersonalizationResearch Papers

Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI LiteratureShuning Zhang, Hui Wang et al. · 2025As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.

HCI & Adaptive InterfacesResearch Papers