Human-Computer Interaction and Visualization
HCI researchers at Google have enormous potential to impact the experience of Google users as well as conduct innovative research. Grounded in user behavior understanding and real use, Google’s HCI researchers invent, design, build and trial large-scale interactive systems in the real world. We declare success only when we positively impact our users and user communities, often through new and improved Google products. HCI research has fundamentally contributed to the design of Search, Gmail, Docs, Maps, Chrome, Android, YouTube, serving over a billion daily users. We are engaged in a variety of HCI disciplines such as predictive and intelligent user interface technologies and software, mobile and ubiquitous computing, social and collaborative computing, interactive visualization and visual analytics. Many projects heavily incorporate machine learning with HCI, and current projects include predictive user interfaces; recommenders for content, apps, and activities; smart input and prediction of text on mobile devices; user engagement analytics; user interface development tools; and interactive visualization of complex data.
Recent Publications
InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Alex Olwal
Ram Iyengar
Na Li
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI), ACM, pp. 23
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Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions. We contribute two large language model (LLM) modules and a code interpreter as part of our framework. The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration. Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workflow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.
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Situationally Induced Impairments and Disabilities (SIIDs) can significantly hinder user experience in everyday activities. Despite their prevalence, existing adaptive systems predominantly cater to specific tasks or environments and fail to accommodate the diverse and dynamic nature of SIIDs. We introduce Human I/O, a real-time system that detects SIIDs by gauging the availability of human input/output channels. Leveraging egocentric vision, multimodal sensing and reasoning with large language models, Human I/O achieves good performance in availability prediction across 60 in-the-wild egocentric videos in 32 different scenarios. Further, while the core focus of our work is on the detection of SIIDs rather than the creation of adaptive user interfaces, we showcase the utility of our prototype via a user study with 10 participants. Findings suggest that Human I/O significantly reduces effort and improves user experience in the presence of SIIDs, paving the way for more adaptive and accessible interactive systems in the future.
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Beyond dashboards: LLM-powered insights for next generation of business intelligence
AIM Research (2024)
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The articles delves into the promise of AI in business intelligence. It briefly reviews the evolution of BI and various Cloud tools, followed by the paradigm shift in how data is consumed. While AI brings huge potential, the article covers areas that enterprises must exercise caution over, when building intelligent agents to answer data questions.
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Solidarity not Charity! Empowering Local Communities for Disaster Relief during COVID-19 through Grassroots Support
Tiffany Knearem
Jeongwon Jo
Oluwafunke Alliyu
John M. Carroll
Computer Supported Cooperative Work (2024) (2024)
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The COVID-19 pandemic brought wide-ranging, unanticipated societal changes as communities rushed to slow the spread of the novel coronavirus. In response, mutual aid groups bloomed online across the United States to fill in the gaps in social services and help local communities cope with infrastructural breakdowns. Unlike many previous disasters, the long-haul nature of COVID-19 necessitates sustained disaster relief efforts. In this paper, we conducted an interview study with online mutual aid group administrators to understand how groups facilitated disaster relief, and how disaster relief initiatives developed and maintained over the course of the first year of COVID-19. Our findings suggest that the groups were crucial sources of community-based support for immediate needs, innovated long-term solutions for chronic community issues and grew into a vehicle for justice-centered work. Our insights shed light on the strength of mutual aid as a community capacity that can support communities to collectively be more prepared for future long-haul disasters than they were with COVID-19.
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Extended reality (XR) has the potential for seamless user interface (UI) transitions across people, objects, and environments. However, the design space, applications, and common practices of 3D UI transitions remain underexplored. To address this gap, we conducted a need-finding study with 11 participants, identifying and distilling a taxonomy based on three types of UI placements --- affixed to static, dynamic, or self entities. We further surveyed 113 commercial applications to understand the common practices of 3D UI mobility control, where only 6.2% of these applications allowed users to transition UI between entities. In response, we built interaction prototypes to facilitate UI transitions between entities. We report on results from a qualitative user study (N=14) on 3D UI mobility control using our FingerSwitches technique, which suggests that perceived usefulness is affected by types of entities and environments. We aspire to tackle a vital need in UI mobility within XR.
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Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction
Shalaleh Rismani
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), Association for Computing Machinery (2024), pp. 1-17 (to appear)
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Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning (ML) systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of workshop data, we highlight theorization of T2I use, harm, and harm-reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts.
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