University of Michigan School of Information
UMSI at CHI 2024: Research, workshops, courses
Wednesday, 05/08/2024
University of Michigan School of Information faculty and PhD students are creating and sharing knowledge that helps build a better world. Here are their publications and the workshops for the May 2024 Conference on Human Factors in Computing Systems (CHI) in Honolulu, Hawaii.
PAPERS
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology
Emma Harvey, Hauke Sandhaus, Abigail Jacobs, Emanuel Moss, Mona Sloane
Motion capture systems, used across various domains, make body representations concrete through technical processes. We argue that the measurement of bodies and the validation of measurements for motion capture systems can be understood as social practices. By analyzing the findings of a systematic literature review (N=278) through the lens of social practice theory, we show how these practices, and their varying attention to errors, become ingrained in motion capture design and innovation over time. Moreover, we show how contemporary motion capture systems perpetuate assumptions about human bodies and their movements. We suggest that social practices of measurement and validation are ubiquitous in the development of data- and sensor-driven systems more broadly, and provide this work as a basis for investigating hidden design assumptions and their potential negative consequences in human-computer interaction.
Show, Not Tell: A Human-AI Collaborative Approach for Designing Sound Awareness Systems
Jeremy Zhengqi Huang, Reyna Wood, Hriday Chhabria, Dhruv Jain
Current sound recognition systems for deaf and hard of hearing (DHH) people identify sound sources or discrete events. However, these systems do not distinguish similar sounding events (e.g., a patient monitor beep vs. a microwave beep). In this paper, we introduce HACS, a novel futuristic approach to designing human-AI sound awareness systems. HACS assigns AI models to identify sounds based on their characteristics (e.g., a beep) and prompts DHH users to use this information and their contextual knowledge (e.g., “I am in a kitchen”) to recognize sound events (e.g., a microwave). As a first step for implementing HACS, we articulated a sound taxonomy that classifies sounds based on sound characteristics using insights from a multi-phased research process with people of mixed hearing abilities. We then performed a qualitative (with 9 DHH people) and a quantitative (with a sound recognition model) evaluation. Findings demonstrate the initial promise of HACS for designing accurate and reliable human-AI systems.
Not Just a Dot on The Map: Food Delivery Workers as Infrastructure
Riyaj Shaikh, Anubha Singh, Barry Brown, Airi Lampinen
Food delivery platforms are location-based services that rely on minimal, quantifiable data points, such as GPS location, to represent and manage labor. Drawing upon an ethnographic study of food delivery work in India during the COVID-19 pandemic, we illustrate the challenges gig workers face when working with a platform that uses their (phone’s) GPS location to monitor and control their movement. Further, we describe how these, along with the platform's opaque, location-based logics, shape the delivery workflow. We also document how the platform selectively represented workers’ bodies during the pandemic to portray them as safe and sterile, describing workers’ tactics in responding to issues arising from asymmetric platform policies. In discussion, we consider what we can learn from understanding gig workers as `infrastructure’, commonly overlooked but visible upon breakdown. We conclude by reflecting on how we might center gig workers’ well-being and bodily needs in design.
Looking Together ≠ Seeing the Same Thing: Understanding Surgeons’ Visual Needs During Intra-operative Coordination and Instruction
Vitaliy Popov, Xinyue Chen, Jingying Wang, Michael Kemp, Gurjit Sandhu, Taylor Kantor, Natalie Meteju, Xu Wang
Shared gaze visualizations have been found to enhance collaboration and communication outcomes in diverse HCI subfields including collaborative work and learning. Given the importance of gaze in surgery operations, especially when a surgeon trainer and trainee need to coordinate their actions, research on the use of gaze to facilitate intra-operative coordination and instruction has been limited and shows mixed implications. We performed a field observation of 8 surgeries and an interview study with 14 surgeons to understand their visual needs during operations, informing ways to leverage and augment gaze to enhance intra-operative coordination and instruction. We found that trainees have varying needs in receiving visual guidance which are often unfulfilled by the trainers’ instructions. It is critical for surgeons to control the timing of the gaze-based visualizations and effectively interpret gaze data. We suggest overlay technologies, e.g., gaze-based summaries and depth sensing, to augment raw gaze in support of surgical coordination and instruction.
InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse
Tao Lu, Hongxiao Zheng, Tianying Zhang, Xuhai “Orson” Xu, Anhong Guo
Smartphone overuse poses risks to people's physical and mental health. However, current intervention techniques mainly focus on explicitly changing screen content (i.e., output) and often fail to persistently reduce smartphone overuse due to being over-restrictive or over-flexible. We present the design and implementation of InteractOut, a suite of implicit input manipulation techniques that leverage interaction proxies to weakly inhibit the natural execution of common user gestures on mobile devices. We present a design space for input manipulations and demonstrate 8 Android implementations of input interventions. We first conducted a pilot lab study (N=30) to evaluate the usability of these interventions. Based on the results, we then performed a 5-week within-subject field experiment (N=42) to evaluate InteractOut in real-world scenarios. Compared to the traditional and common timed lockout technique, InteractOut significantly reduced the usage time by an additional 15.6% and opening frequency by 16.5% on participant-selected target apps. InteractOut also achieved a 25.3% higher user acceptance rate, and resulted in less frustration and better user experience according to participants' subjective feedback. InteractOut demonstrates a new direction for smartphone overuse intervention and serves as a strong complementary set of techniques with existing methods.
“I want it to talk like Darth Vader”: Helping Children Construct Creative Self-Efficacy with Generative AI
Michele Newman, Kaiwen Sun, Ilena B Dallas Gasperina, Grace Y. Shin, Matthew Kyle Pedraja, Ritesh Kanchi, Maia B. Song, Rannie Li, Jin Ha Lee, Jason Yip
The emergence of generative artificial intelligence (GenAI) has ignited discussions surrounding its potential to enhance creative pursuits. However, distinctions between children's and adult's creative needs exist, which is important when considering the possibility of GenAI for children's creative usage. Building upon work in Human-Computer Interaction (HCI), fostering children's computational thinking skills, this study explores interactions between children (aged 7-13) and GenAI tools through methods of participatory design. We seek to answer two questions: (1) How do children in co-design workshops perceive GenAI tools and their usage for creative works? and (2) How do children navigate the creative process while using GenAI tools? How might these interactions support their confidence in their ability to create? Our findings contribute a model that describes the potential contexts underpinning child-GenAI creative interactions and explores implications of this model for theories of creativity, design, and use of GenAI as a constructionist tool for creative self-efficacy.
Better Together: The Interplay Between a Phishing Awareness Video and a Link-centric Phishing Support Tool
Benjamin Berens, Florian Schaub, Mattia Mossano, Melanie Volkamer
Two popular approaches for helping consumers avoid phishing threats are phishing awareness videos and tools supporting users in identifying phishing emails. Awareness videos and tools have each been shown on their own to increase people's phishing detection rate. Videos have been shown to be a particularly effective awareness measure; link-centric warnings have been shown to provide effective tool support. However, it is unclear how these two approaches compare to each other. We conducted a between-subjects online experiment (n=409) in which we compared the effectiveness of the NoPhish video and the TORPEDO tool and their combination. Our main findings suggest that the TORPEDO tool outperformed the NoPhish video and that the combination of both performs significantly better than just the tool. We discuss the implications of our findings for the design and deployment of phishing awareness measures and support tools.
ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-Creation
Xianzhe Fan, Zihan Wu, Chun Yu, Fenggui Rao, Weinan Shi, Teng Tu
The rapid advancement of AI-generated content (AIGC) promises to transform various aspects of human life significantly. This work particularly focuses on the potential of AIGC to revolutionize image creation, such as photography and self-expression. We introduce ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AIGC technologies like Stable Diffusion. ContextCam provides user's image creation process with inspiration by extracting relevant contextual data, and leverages Large Language Model-based (LLM) multi-agents to co-create images with the user. A study with 16 participants and 136 scenarios revealed that ContextCam was well-received, showcasing personalized and diverse outputs as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment when using ContextCam, and acknowledged its ability to inspire creativity.
Authors’ Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts
Taewook Kim, Hyomin Han, Eytan Adar, Matthew Kay, John Joon Young Chung
Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author's vision to the audience's context and taste at scale. However, it is unclear what the authors' values and attitudes would be regarding AI-bridged CLA. To identify these values and attitudes, we conducted an interview study with 18 authors across eight genres (e.g., poetry, comics) by presenting speculative but realistic AI-bridged CLA scenarios. We identified three benefits derived from the dynamics between author, artifact, and audience: those that 1) authors get from the process, 2) audiences get from the artifact, and 3) authors get from the audience. We found how AI-bridged CLA would either promote or reduce these benefits, along with authors' concerns. We hope our investigation hints at how AI can provide intriguing experiences to CLA audiences while promoting authors' values.
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
Paramveer Dhillon, Somayeh Molaei, Jiaqi Li, Maximilian Golub, Shaochun Zheng, Lionel Peter Robert
Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment 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 suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded 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 scaffolding mechanisms.
“I was able to give her the confidence”: Reciprocal Capacity Building in a Community-based Program for Digital Engagement
Julie Hui, Kristin Seefeldt, Lutalo Sanifu, Christie Baer, Jeanette Szomstein, Tawanna R. Dillahunt
Assets-based approaches emphasize the importance of leveraging and building upon community strengths. We describe how a community-based digital capacity building program, Community Tech Workers (CTW), addresses the goals of assets-based development by hiring local residents and students to serve as tech support personnel for underserved minority small business owners in a Midwest city. Through interviews and observations, we examine how reciprocal relationships between tech workers and small business owners are critical to the success and sustainability of the program. We find that tech workers and business owners mutually benefit by 1) building confidence in technology together, 2) having business owners provide reciprocal guidance in professional development, and 3) fostering mutual appreciation and commitment to community development. We conclude by introducing the concept of reciprocal capacity building to HCI and discussing how it provides a potentially more equitable approach to community-based interventions.
Shared Responsibility in Collaborative Tracking for Children with Type 1 Diabetes and their Parents
Yoon Jeong Cha, Yasemin Gunal, Alice Wou, Joyce Lee, Mark Newman, Sun Young Park
Efficient Type 1 Diabetes (T1D) management necessitates comprehensive tracking of various factors that influence blood sugar levels. However, tracking health data for children with T1D poses unique challenges, as it requires the active involvement of both children and their parents. This study aims to uncover the benefits, challenges, and strategies associated with collaborative tracking for children (ages 6-12) with T1D and their parents. Over a three-week data collection probe study with 22 child-parent pairs, we found that collaborative tracking, characterized by the shared responsibility of tracking management and data provision, yielded positive outcomes for both children and their parents. Drawing from these findings, we delineate four distinct tracking approaches: child-independent, child-led, parent-led, and parent-independent. Our study offers insights for designing health technologies that empower both children and parents in learning and encourage the sharing of different perspectives through collaborative tracking.
Understanding the Effect of Reflective Iteration on Individuals’ Physical Activity Planning
Kefan Xu, Xinghui (Erica) Yan, Myeonghan Ryu, Mark W Newman, Rosa Arriaga
Many people do not get enough physical activity. Establishing routines to incorporate physical activity into people's daily lives is known to be effective, but many people struggle to establish and maintain routines when facing disruptions. In this paper, we build on prior self-experimentation work to assist people in establishing or improving physical activity routines using a framework we call “reflective iteration.” This framework encourages individuals to articulate, reflect upon, and iterate on high-level “strategies” that inform their day-to-day physical activity plans. We designed and deployed a mobile application, Planneregy, that implements this framework. Sixteen U.S. college students used the Planneregy app for 42 days to reflectively iterate on their weekly physical exercise routines. Based on an analysis of usage data and interviews, we found that the reflective iteration approach has the potential to help people find and maintain effective physical activity routines, even in the face of life changes and temporary disruptions.
Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning
Jingying Wang, Haoran Tang, Taylor Kantor, Tandis Soltani, Vitaliy Popov, Xu Wang
Videos are prominent learning materials to prepare surgical trainees before they enter the operating room (OR). In this work, we explore techniques to enrich the video-based surgery learning experience. We propose Surgment, a system that helps expert surgeons create exercises with feedback based on surgery recordings. Surgment is powered by a few-shot-learning-based pipeline (SegGPT+SAM) to segment surgery scenes, achieving an accuracy of 92\%. The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons from a formative study. Surgment enables surgeons to 1) retrieve frames of interest through sketches, and 2) design exercises that target specific anatomical components and offer visual feedback. In an evaluation study with 11 surgeons, participants applauded the search-by-sketch approach for identifying frames of interest and found the resulting image-based questions and feedback to be of high educational value.
CareJournal: A Voice-Based Conversational Agent for Supporting Care Communications
John Rudnik, Sharadhi Raghuraj, Mingyi Li, Robin N. Brewer
Effective communication between older adult care recipients and unpaid caregivers is essential to both care partners' well-being. To understand communication in care relationships, we conducted a two-part study with older adult care recipients and caregivers. First, we conducted a two-week diary study to gain insight into care-related communication challenges. While caregivers discussed the benefits of emotional attachment, care recipients expressed concerns about emotional fluctuation and losing autonomy. These findings, along with literature on self-disclosure and conversational scaffolding informed our design of CareJournal—a voice-based conversational agent that supports care-related disclosure between care partners. We evaluated CareJournal with 40 care partners to inform future design considerations and learn more about their communication practices. Our findings highlight the impact of distance and tensions between care and independence, providing insight into how care partners imagine computer-mediated care communication impacting their relationships.
Towards Inclusive Source Code Readability Based on the Preferences of Programmers with Visual Impairments
Maulishree Pandey, Steve Oney, Andrew Begel
Code readability is crucial for program comprehension, maintenance, and collaboration. However, many of the standards for writing readable code are derived from sighted developers' readability needs. We conducted a qualitative study with 16 blind and visually impaired (BVI) developers to better understand their readability preferences for common code formatting rules such as identifier naming conventions, line length, and the use of indentation. Our findings reveal how BVI developers' preferences contrast with those of sighted developers and how we can expand the existing rules to improve code readability on screen readers. Based on the findings, we contribute an inclusive understanding of code readability and derive implications for programming languages, development environments, and style guides. Our work helps broaden the meaning of readable code in software engineering and accessibility research.
“I Did Watch ‘The Handmaid’s Tale”: Threat modeling Privacy Post-Roe in the United States
Nora McDonald, Nazanin Andalibi
Now that the protections of Roe v. Wade are no longer available throughout the United States, the free flow of personal data can be used by legal authorities to provide evidence of felony. However, we know little about how impacted individuals approach their reproductive privacy in this new landscape. We conducted interviews with 15 individuals who may get/were pregnant to address this gap. While nearly all reported deleting period tracking apps, they were not willing to go much further, even while acknowledging the risks of generating data. Quite a few considered a more inhospitable, Handmaid’s Tale like climate in which their medical history and movements would put them in legal peril but felt that, by definition, this reality was insuperable, and also that they were not the target—the notion that privileged location, stage of life did not make them the focus of government or vigilante efforts. We also found that certain individuals (often younger and/or with reproductive risks) were more attuned to the need to modify their technology or equipped to employ high and low-tech strategies. Using an intersectional lens, we discuss implications for media advocacy and propose privacy intermediation to frame our thinking about reproductive privacy.
SQL Puzzles: Evaluating Micro Parsons Problems with Different Feedbacks as Practice for Novices
This paper investigates using micro Parsons problems as a novel practice approach for learning Structured Query Language (SQL). In micro Parsons problems learners arrange predefined code fragments to form a SQL statement instead of typing the code. SQL is a standard language for working with relational databases. Targeting beginner-level SQL statements, we evaluated the efficacy of micro Parsons problems with block-based feedback and execution-based feedback compared to traditional text-entry problems. To delve into learners' experiences and preferences for the three problem types, we conducted a within-subjects think-aloud study with 12 participants. We found that learners reported very different preferences. Factors they considered included perceived learning, task authenticity, and prior knowledge. Next, we conducted two between-subjects classroom studies to evaluate the effectiveness of micro Parsons problems with different feedback types versus text-entry problems for SQL practice. We found that learners who practiced by solving Parsons problems with block-based feedback had a significantly higher learning gain than those who practiced with traditional text-entry problems.
A Design Space for Intelligent and Interactive Writing Assistants
Mina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum, Vipul Reheja, Hua Shen, Subhashini Venugopalan, Thiemo Wambsganss, David Zhou, Emad A. Alghamdi, Tal August, Avinash Bhat, Madiha Zahrah Choksi, Senjuti Dutta, Jin L.C. Guo, Md Naimul Hoque, Yewon Kim, Simon Knight, Seyed Parsa Neshaei, Antonette Shibani, Disha Shrivastava, Lila Shroff, Angina Sergeyuk, Jessi Stark, Sarah Sterman, Siton Wang, Antoine Bosselut, Daniel Buschek, Joseph Chee Chang, Sherol Chen, Max Kreminski, Joonsuk Park, Roy Pea, Eugenia H. Rho, Zhejiang Shen, Pao Siangliulue
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.
Expanding Concepts of Non-Consensual Image-Disclosure Abuse: A Study of NCIDA in Pakistan
Amna Batool, Mustafa Naseem, Kentaro Toyama
Non-Consensual Image-Disclosure Abuse (NCIDA) represents a subset of technology-facilitated sexual abuse where imagery and video with romantic or sexual connotations are used to control, extort, and otherwise harm victims. Despite considerable research on NCIDA, little is known about them in non-Western contexts. We investigate NCIDA in Pakistan, through interviews with victims, their relatives, and investigative officers; and observations of NCIDA cases being processed at a law enforcement agency. We find, first, that what constitutes NCIDA is much broader in Pakistan's patriarchal society, and that its effects can be more severe than in Western contexts. On every dimension -- types of content, perpetrators, impact on victims, and desired response by victims -- our findings suggest an expansion of the concepts associated with NCIDA. We conclude by making technical and policy-level recommendations, both to address the specific context of Pakistan, and to enable a more global conception of NCIDA.
ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events
Woo Suk Seo, Chanmo Yang, Young-Ho Kim
Children typically learn to identify and express their emotions by sharing stories and feelings with others, particularly family members. However, it is challenging for parents or siblings to have effective emotion communication with children since children are still developing their communication skills. We present ChaCha, a chatbot that encourages and guides children to share personal events and associated emotions. ChaCha combines a state machine and large language models (LLMs) to keep the dialogue on track while carrying on free-form conversations. Through an exploratory study with 20 children (aged 8-12), we examine how ChaCha prompts children to share personal events and guides them to describe associated emotions. Participants perceived ChaCha as a close friend and shared their stories on various topics, such as family trips and personal achievements. Based on the findings, we discuss opportunities for leveraging LLMs to design child-friendly chatbots to support children in sharing emotions.
Digital Repression in Palestine
While Israeli suppression of Palestinian voices is well-understood, much less is known about the Palestinian authorities’ repression of Palestinians – the very people they are supposed to represent. This paper investigates digital repression by Hamas and the Pales- tinian Authority through semi-structured interviews – in-person and online – with 19 Palestinian activists who post on social media. Many of our findings echo those from other repressive contexts, but the unusual Palestinian context also gives rise to several unique elements. For example, Palestinian authorities, while incorporating some high-tech methods, appear to rely primarily on a low-tech, labor-intensive apparatus to monitor, intimidate, and censor their targets, some of which involves highly personalized forms of repres- sion. We also heard credible accusations of Palestinian authorities’ collaboration with Iranian and Israeli governments, the latter typ- ically viewed as an adversary by Palestinians. We consider the implications of these findings and offer recommendations both for activists and social media platforms
The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses
Jordyn Young, Laala M Jawara, Diep N Nguyen, Brian Daly, Jina Huh-Yoo, Afsaneh Razi
Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.
“I know even if you don’t tell me”: Understanding Users’ Privacy Preferences Regarding AI-based Inferences of Sensitive Information for Personalization
Sumit Asthana, Jane Im, Zhe Chen, Nikola Banovic
Personalization improves user experience by tailoring interactions relevant to each user’s background and preferences. However, personalization requires information about users that platforms often collect without their awareness or their enthusiastic consent. Here, we study how the transparency of AI inferences on users’ personal data affects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for personalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly provided data after awareness of inferences. Our results show that participants chose restrictive consent decisions for sensitive and incorrect inferences about them and for their answers that led to such inferences. Our findings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences.
Jigsaw: Authoring Immersive Storytelling Experiences with Augmented Reality and Internet of Things
Lei Zhang, Daekun Kim, Youjean Cho, Ava Robinson, Yu Jiang Tham, Rajan Vaish, Andrés Monroy-Hernández.
Augmented Reality (AR) presents new opportunities for immersive storytelling. However, this immersiveness faces two main hurdles. First, AR's immersive quality is often confined to visual elements, such as pixels on a screen. Second, crafting immersive narratives is complex and generally beyond the reach of amateurs due to the need for advanced technical skills. We introduce Jigsaw, a system that empowers beginners to both experience and craft immersive stories, blending virtual and physical elements. Jigsaw uniquely combines mobile AR with readily available Internet-of-things (IoT) devices. We conducted a qualitative study with 20 participants to assess Jigsaw's effectiveness in both consuming and creating immersive narratives. The results were promising: participants not only successfully created their own immersive stories but also found the playback of three such stories deeply engaging. However, sensory overload emerged as a significant challenge in these experiences. We discuss design trade-offs and considerations for future endeavors in immersive storytelling involving AR and IoT.
WORKSHOPS
Sustaining Scalable Sustainability: Human-Centered Green Technology for Community-wide Carbon Reduction
Vikram Mohanty, Jingchao Fang, Song Mi Lee-Kan, Hamed Alavi, Joaquin Salas, Genevieve Patterson, Elizabeth F Churchill, Charlene C. Wu, David A. Shamma
Escalating global CO2 emissions highlights the immediate need for scalable sustainable practices. Corporate and policy roles aside, there's a need for carbon-neutrality-based systems and practices to bridge the disconnect between actions and the perceived impact on the environment. This one-day workshop focuses on individuals and communities, advocating for human-centered tools to bridge this awareness-action gap. While Human-AI Interaction (HAI), cognitive science theories, and social computing tools have shown promise in various domains, their potential remains largely unexplored in the context of sustainability. This workshop aims to delve into these avenues for crafting tractable systems for effective, contextually relevant interventions and driving sustainable behaviors. By engaging multidisciplinary researchers, we aim to intertwine local insights with behavioral theory and technology, fostering intrinsic carbon literacy and a sustainability ethos, ensuring lasting and scalable impacts.
HCI and Aging: New Directions, New Principles
Bran Knowles, Aneesha Singh, Aloha Hufana Ambe, Robin N. Brewer, Amanda Lazar, Helen Petrie, John Vines, Jenny Waycott
Concerns regarding the impacts of stereotyped, deficit-based, and problem-oriented approaches to older adult users have propelled HCI to explore new understandings and ways of approaching aging as a subject in recent years. Meanwhile, older adults' relationships with digital technologies are also evolving, driven both by technological advancements and the destabilizing experience of the global pandemic. Now is an important time to take stock of these changes and their significance to the field of HCI and Aging. This workshop attends, therefore, to the need for collective reflection on where the field is now, how we got here, and where it is heading. In addition to highlighting emerging areas requiring research attention, the workshop will produce a snapshot in time to compare with several years hence as the field continues to evolve. The second part of the workshop responds to the need for a clear alternative to deficit based approaches to designing technologies for older adult users. We will pool the collective wisdom of the HCI and Aging community to generate a set of principles to guide research and development toward maximization of benefit and minimization of harm to older adult users/stakeholders.
Methods for Family-Centered Design: Bridging the Gap between Research and Practice
Bengisu Cagiltay, Hui-Ru Ho, Kaiwen Sun, Zhaoyuan Su, Yuxing Wu, Olivia K. Richards, Qiao Jin, Junnan Yu, Jerry Alan Fails, Jason Yip, Jodi Forlizzi
Technology is pervasive in family life. Family-centered design can enable the creation of technological solutions that align with the diverse needs of and dynamics within families. Yet, designing meaningful interactive technologies that are useful for and desired by families remains a complex and evolving challenge. Furthermore, there are limited resources in the HCI community examining theoretical, methodological, and practical processes for designing and testing technology supporting family life (e.g., interactions among parents, children, siblings, older adults). This workshop aims to bridge this gap by bringing together researchers and practitioners from interdisciplinary areas to discuss practical approaches in applying effective methods, theories, and tools for designing technology for and with families. The main goal of this workshop is to collaborate on creating a knowledge base for family-centered design. The workshop will aim to provide valuable opportunities for researchers and practitioners to grow a community, exchange insights, and share best practices.
Designing Inclusive Future Augmented Realities
Michael Nebeling, Mika Oki, Mirko Gelsomini, Gillian R. Hayes, Mark Billinghurst, Kenji Suzuki, Roland Graf
Augmented and mixed reality technology is rapidly advancing, driven by innovations in display, sensing, and AI technologies. This evolution, particularly in the era of generative AI with large language and text-to-image models such as GPT and Stable Diffusion, has the potential, not only to make it easier to create, but also to adapt and personalize, new content. Our workshop explores the pivotal role of augmented and mixed reality to shape a user's interactions with their physical surroundings. We aim to explore how inclusive future augmented realities can be designed, with increasing support for automation, such that environments can welcome users with different needs, emphasizing accessibility and inclusion through layers of augmentations. Our aim is not only to remove barriers by providing accommodations, but also to create a sense of belonging by directly engaging users. Our workshop consists of three main activities: (1) Through brainstorming and discussion of examples provided by the workshop organizers and participants, we critically review the landscape of accessible and inclusive design and their vital role in augmented and mixed reality experiences. (2) Through rapid prototyping activities including bodystorming and low-fidelity, mixed-media prototypes, participants explore how augmented and mixed reality can transform physical space into a more personal place, enhancing accessibility and inclusion based on novel interface and interaction techniques that are desirable, but not necessarily technically feasible just yet. In the workshop, we plan to focus on physical space to facilitate rapid prototyping without technical constraints, but techniques developed in the workshop are likely applicable to immersive virtual environments as well. (3) Finally, we collaborate to outline a research agenda for designing future augmented realities that promote equal opportunities, benefiting diverse user populations. Our workshop inspires innovation in augmented and mixed reality, reshaping physical environments to be more accessible and inclusive through immersive design.
Workshop on Theory of Mind in Human-AI Interaction
Qiaosi Wang (Chelsea), Sarah E. Walsh, Mei Si, Jeffrey O. Kephart, Justin D. Weisz,
Theory of Mind (ToM) refers to humans’ capability of attributing mental states such as goals, emotions, and beliefs to ourselves and others. This concept has become of great interest in human-AI interaction research. Given the fundamental role of ToM in human social interactions, many researchers have been working on methods and techniques to equip AI with an equivalent of human ToM capability to build highly socially intelligent AI. Another line of research on ToM in human-AI interaction aims at providing human-centered AI design implications through exploring people’s tendency to attribute mental states such as blame, emotions, and intentions to AI, along with the role that AI should play in the interaction (e.g., as a tool, partner, teacher, and more) to align with people’s expectations and mental models.
Together, these two research perspectives on ToM form an emerging paradigm of “Mutual Theory of Mind (MToM)” in human-AI interaction, where both the human and the AI each possess some level of ToM-like capability during interactions.
The goal of this workshop is to bring together researchers working on different perspectives of ToM in human-AI interaction to define a unifying research agenda on the human-centered design and development of Mutual Theory of Mind (MToM) in human-AI interaction. We aim to explore three broad topics to inspire workshop discussions:
- Designing and building AI’s ToM-like capability
- Understanding and shaping human’s ToM in human-AI interaction
- Envisioning MToM in human-AI interaction
DOCTORAL CONSORTIUM
Understanding and Supporting Financially-Constrained Aspiring Entrepreneurs’ Entrepreneurial Transitions
Entrepreneurship is perceived as a promising path to financial stability, especially for those with financial constraints. Still, the transition to entrepreneurship is not easy. Financially-constrained aspiring entrepreneurs, many of whom are racial minorities, must overcome many challenges to achieve their goals. This includes obtaining access to mentorship, financial capital, and digital support for their businesses. In my dissertation, I examine how existing sociotechnical interventions, including social media and community-based-organization (CBO)-supported interventions, can support their entrepreneurial transitions, especially as a provision of informational and emotional support. Early findings from my dissertation research suggest that CBO-supported peer group interventions provide a source of informational and emotional support, helping financially-constrained aspiring entrepreneurs make progress toward their goals and overcome setbacks. In my dissertation, I aim to unpack the factors that contribute to the success of such groups and contribute design opportunities for sociotechnical interventions to better support financially-constrained aspiring entrepreneurs' work-role transition processes.
ALT CHI
Overworking in HCI: A Reflection on Why We Are Burned Out, Stressed, and Out of Control; and What We Can Do About It
Abraham Mhaidli, Kat Roemmich
In this alt.chi submission, we explore overwork in academic Human-Computer Interaction (HCI) research. We first ask why it is that we overwork: a combination of external pressures including cutthroat publication-centric competition, lack of recognition for invisible research labor facilitated by technologies that promote overwork and further hide the labor behind research, and institutionalized overwork norms reified through toxic advising practices; along with internal pressures, including information opacity and precarious employment as tools for self-exploitation, intense personal and emotional investment in research, and our relational commitments to each other. We explore overwork's detrimental consequences to individual researchers, the relationships between them, and research integrity. Our analysis of overwork in academia underscores the urgent need to halt our overwork norms and pivot towards reasonable, responsible, and health-conscious work practices---before we burn to a crisp in the name of more publications.
LATE BREAKING WORK
Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices
Ruiwei Xiao, Xinying Hou, John Stamper
Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving. However, most existing LLM-based hint systems are limited to one single hint type. To investigate whether and how different levels of hints can support students' problem-solving and learning, we conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system providing four levels of hints from general natural language guidance to concrete code assistance, varying in format and granularity. We discovered that high-level natural language hints alone can be helpless or even misleading, especially when addressing next-step or syntax-related help requests. Adding lower-level hints, like code examples with in-line comments, can better support students. The findings open up future work on customizing help responses from content, format, and granularity levels to accurately identify and meet students' learning needs.
Trust and Transparency: An Exploratory Study on Emerging Adults’ Interpretations of Credibility Indicators on Social Media Platforms
Erica Shusas, Andrea Forte
The misinformation crisis across social media has disrupted critical access to information in health, politics, and public safety. Content labels have become a feature that social media platforms use to signal credibility of social media posts. Young adults receive a proportionally high percentage of their news through social media platforms, yet prior work has shown that credibility indicators are not effective signals for young audiences. This late-breaking work presents initial findings from an exploratory study into how emerging adults (ages 18-25) assess different credibility indicators currently used on social media platforms. Our findings indicate that participants have a wide variety of interpretations of the purpose and source of context labels, are supportive of automated approaches to content labeling, and trust social media platforms to oversee the application of content labels. This paper contributes these findings to the growing scholarship on content labeling and discusses their implications for designers and policymakers.
Support in Short Form: Investigating TikTok Comments on Videos with #Harassment
Atieh Armin, Joseph J Trybala, Jordyn Young, Afsaneh Razi
Exploring the dynamics of public discourse on social media reveals critical insights into how topics like harassment are perceived, discussed, and handled within online communities. To understand these dynamics within multimodal short-form video-based communities, we conducted topic modeling on 145,515 comments of videos tagged with #harassment on TikTok. We identified nine topics, including community responses to harassment and threats, law enforcement responses to harassment, and discussions around self-defense strategies. Our findings revealed the diverse nature of online discussions about harassment, containing empathy, polarization, frustration, and humor. These various topics underscore the significant role of TikTok as a platform for shaping public opinions on critical social issues and amplifying the voices of victims. This paper contributes to understanding how public discourse on harassment unfolds in TikTok to inform future research and policy-making to ensure safer online communities. Content Warning: This paper includes sensitive topics such as harassment, reader discretion is advised.
Supporting the Digital Aspects of Reentry for Formerly Incarcerated Individuals
Ihudiya Finda Ogbonnaya-Ogburu, Aarti Israni
For formerly incarcerated individuals, reintegrating into society and learning to use digital tools for everyday tasks is essential. While reentry nonprofit programs provide social support, there is limited research on the specific strategies they use to help formerly incarcerated individuals overcome digital challenges associated with adjusting to life after prison. To address this gap, we conducted semi-structured interviews with eight nonprofit employees to understand how they support returning citizens in the digital aspects of reentry. Our research revealed that practicing self-reflection, being present, improvised teaching, and leveraging offline networks are important strategies used by these organizations. Our findings offer fresh perspectives on how these organizations aid formerly incarcerated individuals in their digital reentry process.
Tangible Stats: An Embodied and Multimodel Platform for Teaching Data and Statistics to Blind and Low Vision Students
Danyang Fan, Olivia Tomassetti, Sile O’Modhrain, Sean Follmer, Gene S-H Kim, Shloke Nirav Patel, Victor Lee
Interactive data learning tools provide explorable ways for students to build intuitions about data, data representations, and statistical parameters. However, these tools rely on visual consumption and are not accessible to blind and low vision (BLV) students. In this work, we investigate opportunities to leverage active exploration, enriched with multimodal feedback and embodied interaction, to foster an understanding of the relationships among individual data values, data representations, and statistical measures. We explore these opportunities in the form of an accessible learning platform that allows students to hear and feel how statistical measures are changing in real time as they construct and manipulate physicalized data representations. We introduced the platform to four teachers of students with visual impairments (TVIs) through a two-hour-long focus group. TVIs embraced the platform's exploratory nature and universality and recommended the consideration of additional auditory and texture-based interactions to enhance engagement.
Toward a Measure of Collective Digital Capacity: An Exploratory Analysis
Tawanna R Dillahunt, Kerby Shedden, Mila Ekaterina Filipof, Soyoung Lee, Mustafa Naseem, Kentaro Toyama, Julie Hui
Digital training initiatives must shift toward critical cultural and social practices that encourage full participation in community affairs. However, no measure exists to account for digital capacity at the community level. Thus, we present this late-breaking work to begin designing and validating a measure of community digital capacity and report the results of an exploratory factor analysis. The analysis is based on 553 respondents across the United States to estimate an initial three-factor structure of (1) social digital capacity, (2) individual digital capacity, and (3) infrastructure. Such questions address limitations with existing theories that do not show digital inequities in the context of underlying systemic and structural challenges posed by one's social position. Our preliminary results suggest a potential measure for researchers and practitioners to understand whether people can access shared digital resources and activities with acceptable scientific guarantees, including favorable Akaike and Bayesian information criteria.
Understanding How to Design a Social computing System That Helps PhD Students Collectively Navigate Mistreatment or Abuse in Advising Relationships
People in power causing harm to those with less power is a long-standing problem across organizations. Academia is no exception. When advisors mistreat or abuse PhD students, how could a digital platform help affected PhD students connect with each other for collectively exploring solutions? To understand if there is a need for such a system, and how to design it, we conducted interviews with 10 PhD students. Our findings showed participants were overall positive about the high-level concept of a system for connecting PhD students to address problematic advising. Participants emphasized various social and technical features needed for comfortably using such a system. Simultaneously, participants had different preferences on how they would use it, based on their risk levels. We conclude by reflecting on the importance of centering users’ consent in nuanced ways when actually building the system.
JOURNALS
Awareness, Intention, (In)Actions: Individuals’ Reactions to Data Breaches
Peter Mayer, Yixin Zou, Byron M. Lowens, Hunter A Dyer, Khue Le, Florian Schaub, Adam J Aviv
Data breaches are prevalent. We provide novel insights into individuals’ awareness, perception, and responses to breaches that affect them through two online surveys: a main survey (𝑛=413) in which we presented participants with up to three breaches that affected them, and a follow-up survey (𝑛=108) in which we investigated whether the main study participants followed through with their intentions to act. Overall, 73% of participants were affected by at least one breach, but participants were unaware of 74% of breaches affecting them. While some reported intention to take action, most participants believed the breach would not impact them. We also found a sizeable intention-behavior gap. Participants did not follow through with their intention when they were apathetic about breaches, considered potential costs, forgot, or felt resigned about taking action. Our findings suggest that breached organizations should be held accountable for more proactively informing and protecting affected consumers.
Examining Voice Community Use
Robin Brewer, Sam Addison Ankenbauer, Manahil Hasmi, Pooja Upadhyay
Visual online communities can present accessibility challenges to older adults or people with vision and motor disabilities. Motivated by this challenge, accessibility and HCI researchers have called for voice-based communities to support aging and disability. This paper extends prior work on voice community design and short-term use by providing empirical data on how people interact with voice communities over time and intentional instances of non-use. We conducted a one-year study with 43 blind and low vision older adults, of whom 21 used a voice-based community. We use vignettes to unpack five different voice community member roles - the obligatory poster, routine poster, cross-platform lurker, busy socialite, and visual expertise seeker - and discuss community interactions over time. Findings show how participation varied based on engagement in other communities and ways that participants sought interaction. We discuss (1) how to design voice communities for member roles and (2) the implications of synchronous and asynchronous voice community interaction in voice-only communities.
PANELS
Improving Advising Relationships Between PhD Students and Faculty in Human-computer Interaction
Jane Im, Himanshu Zade, Steve Oney, Pamela J. Wisniewski, Kentaro Toyama
Advisor-advisee relationships between PhD students and faculty are vital to research, but advising dynamics can be challenging for both student and advisor. Though advising can involve egregious problems such as sexual harassment, we focus on what might be less serious but more common issues such as exploitation, unprofessional behavior, mishandling of credit, and inadequate communication. While problems can be caused by advisor or advisee, the power imbalance exacerbates problems for PhD students. In any case, open discussion about PhD advising is rare. In this panel, we hope to start a much-needed conversation about PhD advising to raise awareness within the SIGCHI community about common advising problems; and to begin brainstorming solutions that faculty, administrators, and PhD students can implement.
COMPETITIONS
Where’s the Water? Supporting Clean Water Access for the Homeless Community
Alexandra Balmaceda, Ziwei Chen
Access to clean water is essential, yet it poses a significant challenge for the homeless population. Our project, 'Where’s the Water,' is a web-based tool designed to improve water access for the homeless community. It maps nearby clean water sources like drinking fountains, public restrooms, and showers. The tool’s design was informed through interviews with the homeless community in Ann Arbor, Michigan. The insights gained from these interviews were further supported by key findings from recent studies related to homelessness and water access. Besides locating, our tool’s functionality also includes filtering sources for operational hours and water quality. It features crowd-sourcing, allowing users to add new sources on the map, effectively utilizing community knowledge. In this article, we describe our research and design approach, highlighting the community and organizational feedback that helped turn our concept into a useful tool.
