CMU at CHI 2024

CMU at CHI 2024 kharlan Sat, 05/11/2024 - 09:08 May 12, 2024 News Researchers from the Human-Computer Interaction Institute (HCII) and several other Carnegie Mellon University schools and disciplines contributed to more than 40 papers accepted to the 2024 CHI Conference on Human Factors in Computing Systems. The Association of Computing Machinery (ACM) conference on computer human interaction, commonly referred to as “CHI” (pronounced “kai”) for short, will take place from May 11-16, 2024, in Honolulu, Hawaiʻi. "The HCI Institute continues to do world-class research and we are delighted to showcase so much of our work at this conference,” said Brad Myers , HCII Geschke Director and Professor. “CHI encompasses the research of many of our faculty and students, and has historically been the premiere place to publish work in many areas of human-computer interaction. I'm looking forward to connecting with colleagues and our alumni at the conference." We celebrate the following awards and research affiliated with Carnegie Mellon University authors at CHI 2024: SIGCHI Awards Congratulations to the following 2024 SIGCHI awardees from the HCII: Jodi Forlizzi , the Herbert A. Simon Professor in Computer Science and HCII and associate dean for diversity, equity and inclusion in the School of Computer Science (SCS), received the 2024 Lifetime Research Award. Amy Ogan , associate professor of learning sciences, received the 2024 Societal Impact Award. HCII alumnus Karan Ahuja (SCS 2023) received a 2024 Outstanding Dissertation Award for his thesis, "Practical and Rich User Digitization" (pdf). For more info, visit: Forlizzi, Ogan and Ahuja Receive 2024 SIGCHI Awards or the 2024 SIGCHI Awards announcement . Special Event A new book from Brad A. Myers explores the history, current and future design of interaction techniques. Myers will be hosting a book signing event on May 13 and teaching a short course on the subject (Course #C04: Interaction Techniques – History, Design and Evaluation) on May 14. Learn more: Myers Publishes Book on Interaction Techniques Papers and Paper Awards We are proud to share that Carnegie Mellon University authors from a variety of schools and disciplines contributed to more than 40 papers accepted to CHI 2024, including three Best Paper and five Honorable Mention Awards. A list of accepted papers with CMU contributing authors is available below in alphabetical order by paper title, and their award winning papers are indicated by the following icons: Icon represents a Best Paper Award Icon represents a Best Paper Honorable Mention Award __________ “Don’t Put All Your Eggs In One Basket”: How Cryptocurrency Users Choose and Secure Their Wallets Yaman Yu, Tanusree Sharma, Sauvik Das , Yang Wang Abstract:  Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings. "If This Person is Suicidal, What Do I Do?": Designing Computational Approaches to Help Online Volunteers Respond to Suicidality Logan Stapleton, Sunniva Liu, Cindy Liu, Irene Hong, Stevie Chancellor, Robert E Kraut , and Haiyi Zhu Abstract:  Online platforms provide support for many kinds of distress, including suicidal thoughts and behaviors. However, because many platforms restrict suicidal talk, volunteers on these platforms struggle with how to help suicidal people who come for support. We interviewed 11 volunteer counselors in a large online support platform, including after they role-played conversations with varying severities of suicidality, to explore practices and challenges when identifying and responding to suicidality. We then presented Speed Dating design concepts around emotional preparation and support, real-time guidance, training, and suicide detection. Participants wanted more support and preparation for conversations with suicidal people, but were conflicted about AI-based technologies, including trade-offs between potential benefits of conversational agents for training and limitations of prediction or real-time response suggestions, due to the sensitive, context-dependent decisions that volunteers must make. Our work has important implications for nuanced considerations and design choices around developing digital mental health technologies. "It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents Zhiping Zhang, Michelle Jia , Hao-Ping (Hank) Lee , Bingsheng Yao, Sauvik Das , Ada Lerner, Dakuo Wang, Tianshi Li Abstract:  The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigm shifts to protect the privacy of LLM-based CA users. "It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance JiWoong (Joon) Jang, Sanika Moharana, Patrick Carrington, and Andrew Begel Abstract:  Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences. “The bus is nothing without us”: Making Visible the Labor of Bus Operators amid the Ongoing Push Towards Transit Automation" Hunter Akridge , Bonnie Fan , Alice Xiaodi Tang , Chinar Mehta, Nikolas Martelaro , and Sarah E. Fox Abstract:  This paper describes how the complexity of circumstances bus operators manage presents unique challenges to the feasibility of high-level automation in public transit. Avoiding an overly rationalized view of bus operators' labor is critical to ensure the introduction of automation technologies does not compromise public wellbeing, the dignity of transit workers, or the integrity of critical public infrastructure. Our findings from a group interview study show that bus operators take on work — undervalued by those advancing automation technologies — to ensure the well-being of passengers and community members. Notably, bus operators are positioned to function as shock absorbers during social crises in their communities and in moments of technological breakdown as new systems come on board. These roles present a critical argument against the rapid push toward driverless automation in public transit. We conclude by identifying opportunities for participatory design and collaborative human-machine teaming for a more just future of transit. A Contextual Inquiry of People with Vision Impairments in Cooking Franklin Mingzhe Li , Michael Xieyang Liu , Shaun K. Kane, Patrick Carrington Individuals with vision impairments employ a variety of strategies for object identification, such as pans or soy sauce, in the culinary process. In addition, they often rely on contextual details about objects, such as location, orientation, and current status, to autonomously execute cooking activities. To understand how people with vision impairments collect and use the contextual information of objects while cooking, we conducted a contextual inquiry study with 12 participants in their own kitchens. This research aims to analyze object interaction dynamics in culinary practices to enhance assistive vision technologies for visually impaired cooks. We outline eight different types of contextual information and the strategies that blind cooks currently use to access the information while preparing meals. Further, we discuss preferences for communicating contextual information about kitchen objects as well as considerations for the deployment of AI-powered assistive technologies. An Evidence-based Workflow for Studying and Designing Learning Supports for Human–AI Co-creation (Late-Breaking Work) Frederic Gmeiner , Jamie Conlin, Eric Tang , Nikolas Martelaro , Kenneth Holstein Abstract:  Generative artificial intelligence (GenAI) systems introduce new possibilities for enhancing professionals’ workflows, enabling novel forms of human–AI co-creation. However, professionals often struggle to learn to work with GenAI systems effectively. While research has begun to explore the design of interfaces that support users in learning to co-create with GenAI, we lack systematic approaches to investigate the effectiveness of these supports. In this paper, we present a systematic approach for studying how to support learning to co-create with GenAI systems, informed by methods and concepts from the learning sciences. Through an experimental case study, we demonstrate how our approach can be used to study and compare the impacts of different types of learning supports in the context of text-to-image GenAI models. Reflecting on these results, we discuss directions for future work aimed at improving interfaces for human–AI co-creation. Are Robots Ready to Deliver Autism Inclusion?: A Critical Review Naba Rizi, William Wu, Mya Bolds, Raunak Mondal, Andrew Begel , and Imani N. S. Munyaka Abstract:  The marginalization of autistic people in our society today is multi-faceted as it includes violence that is both physical and ideological in nature. It is rooted in the dehumanization, infantilization, and masculinization of autistic people and pervasive even in contemporary research studies that continue to echo ableist ideologies from the past. In this work, we identify how HRI research reproduces systemic social inequalities and explain how they align with historical misrepresentations, and other systemic barriers. We analyzed 142 papers focusing on HRI and autism published between 2016 and 2022. We critique these studies through a mixed-methods analysis of their definition of autism, study designs, participant recruitment, and results. Our findings indicate that HRI research stigmatizes autism in three dimensions - 1) the pathologization of autism, 2) gender and age-based essentialism, and 3) power imbalances. Our work uncovered that about 90% of HRI research during the timeline explored excluded the perspectives of autistic people, particularly those from understudied groups. We recommend broadening the inclusion of autistic people, considering research objectives beyond clinical use, and diversifying collaborations, foundational works considered, & participant demographics for more inclusive future work. BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and Ideation (Late-Breaking Work) Hyeonsu B. Kang , David Chuan-En Lin , Nikolas Martelaro , Aniket Kittur , Yan-Ying Chen, Matthew K. Hong Abstract:  Nature often inspires solutions for complex engineering problems, but it is challenging for designers to discover relevant analogies and synthesize from them. Here, we present an end-to-end system, BioSpark, that generates biological-analogical mechanisms and provides an interactive interface for comprehension and ideation. From a small seed set of expert-curated mechanisms, BioSpark's pipeline iteratively expands them by constructing and traversing organism taxonomies, aiming to overcome both data sparsity in expert curation and limited conceptual diversity in purely automated analogy generation. The interface helps designers recognize and understand relevant analogs to design problems using four interaction features. We conduct an exploratory study with design students to showcase how BioSpark facilitated analogical transfer of ideas but was limited in conveying active ingredients, the core abstraction underpinning how mechanisms work. We discuss this limitation and other implications such as generative hallucination that could facilitate shifts in human exploration of new design spaces. Bring Privacy To The Table: Interactive Negotiation for Privacy Settings of Shared Sensing Devices Haozhe Zhou, Mayank Goel, Yuvraj Agarwal Abstract:  To address privacy concerns with the Internet of Things (IoT) devices, researchers have proposed enhancements in data collection transparency and user control. However, managing privacy preferences for shared devices with multiple stakeholders remains challenging. We introduced ThingPoll, a system that helps users negotiate privacy configurations for IoT devices in shared settings. We designed ThingPoll by observing twelve participants verbally negotiating privacy preferences, from which we identified potentially successful and inefficient negotiation patterns. ThingPoll bootstraps a preference model from a custom crowdsourced privacy preferences dataset. During negotiations, ThingPoll strategically scaffolds the process by eliciting users’ privacy preferences, providing helpful contexts, and suggesting feasible configuration options. We evaluated ThingPoll with 30 participants negotiating the privacy settings of 4 devices. Using ThingPoll, participants reached an agreement in 97.5% of scenarios within an average of 3.27 minutes. Participants reported high overall satisfaction of 83.3% with ThingPoll as compared to baseline approaches. ClassInSight: Designing Conversation Support Tools to Visualize Classroom Discussion for Personalized Teacher Professional Development Tricia J. Ngoon , S Sushil, Angela Stewart, Ung-Sang Lee, Saranya Venkatramen, Neil Thawani , Prasenjit Mitra, Sherice Clarke, John Zimmerman , Amy Ogan Abstract:  Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important. Co-design Accessible Public Robots: Insights from People with Mobility Disability, Robotic Practitioners and Their Collaborations Howard Ziyu Han , Franklin Mingzhe Li , Alesandra Baca Vazquez, Daragh Byrne , Nikolas Martelaro , Sarah E Fox Abstract:  Sidewalk robots are increasingly common across the globe. Yet, their operation on public paths poses challenges for people with mobility disabilities (PwMD) who face barriers to accessibility, such as insufficient curb cuts. We interviewed 15 PwMD to understand how they perceive sidewalk robots. Findings indicated that PwMD feel they have to compete for space on the sidewalk when robots are introduced. We next interviewed eight robotics practitioners to learn about their attitudes towards accessibility. Practitioners described how issues often stem from robotic companies addressing accessibility only after problems arise. Both interview groups underscored the importance of integrating accessibility from the outset. Building on this finding, we held four co-design workshops with PwMD and practitioners in pairs. These convenings brought to bear accessibility needs around robots operating in public spaces and in the public interest. Our study aims to set the stage for a more inclusive future around public service robots. COMPA: Using Conversation Context to Achieve Common Ground in AAC Stephanie Valencia, Jessica Huynh , Emma Yiang , Yufei Wu , Teresa Wan , Zixuan Zheng , Henny Admoni , Jeffrey Bigham , Amy Pavel Abstract:  Group conversations often shift quickly from topic to topic, leaving a small window of time for participants to contribute. AAC users often miss this window due to the speed asymmetry between using speech and using AAC devices. AAC users may take over a minute longer to contribute, and this speed difference can cause mismatches between the ongoing conversation and the AAC user's response. This results in misunderstandings and missed opportunities to participate. We present COMPA, an add-on tool for online group conversations that seeks to support conversation partners in achieving common ground. COMPA uses a conversation's live transcription to enable AAC users to mark conversation segments they intend to address (Context Marking) and generate contextual starter phrases related to the marked conversation segment (Phrase Assistance) and a selected user intent. We study COMPA in 5 different triadic group conversations, each composed by a researcher, an AAC user and a conversation partner (n=10) and share findings on how conversational context supports conversation partners in achieving common ground. ConeAct: A Multistable Actuator for Dynamic Materials Yuyu Lin , Jesse T. Gonzalez , Zhitong Cui, Yash Rajeev Banka , Alexandra Ion Abstract:  Complex actuators in a small form factor are essential for dynamic interfaces. In this paper, we propose ConeAct, a cone-shaped actuator that can extend, contract, and bend in multiple directions to support rich expression in dynamic materials. A key benefit of our actuat