When working on digital devices, people often face distractions that can lead to a decline in productivity and efficiency, as well as negative psychological and emotional impacts. To address this challenge, we introduce a novel Artificial Intelligence (AI) assistant that elicits a user’s intention, assesses whether ongoing activities are in line with that intention, and provides gentle nudges when deviations occur. The system leverages a large language model to analyze screenshots, application titles, and URLs, issuing notifications when behavior diverges from the stated goal. Its detection accuracy is refined through initial clarification dialogues and continuous user feedback. In a three-week, within-subjects field deployment with 22 participants, we compared our assistant to both a rule-based intent reminder system and a passive baseline that only logged activity. Results indicate that our AI assistant effectively supports users in maintaining focus and aligning their digital behavior with their intentions. Our source code is publicly available at https://intentassistant.github.io

As misinformation proliferates online, large language models (LLMs) have been proposed as a promising tool to accelerate fact-checking workflows. While LLMs demonstrate strong performance in tasks such as text annotation, their capabilities in generating fact-checking reports remain uncertain. To investigate how media experts evaluate LLM-generated fact-checking reports, we conducted a 2 (Source: human vs. LLM) X 2 (Disclosure of Source: yes or no) between-subjects online experiment with media professionals (N=274). Our analyses reveal that experts perceive LLM-generated reports as significantly less useful than human-written reports; and such differences become larger when participants are not aware of the source. However, LLM-generated fact-checking reports were rated as accurate and logical as human-authored ones. Party affiliation plays a role in predicting perceived logicalness. Our findings advance the understanding of experts’ evaluation of LLM-generated content within the context of misinformation, which provides important theoretical contributions to HCI and communication theories as well as practical implications for the field.

Platform workers often experience isolation in their work. They use online forums to connect, but their moderation remains underexplored. Article 20 of the EU Platform Work Directive requires digital labour platforms to provide workers with a “communication channel” and leaves interpretation for how to design it up to the platforms. To inform this issue, we qualitatively analyse community rules and moderator comments across 28 worker subreddits. We show how moderators work to reduce harms such as racism and doxxing, cultivate their community through curation, and decide whether to enforce or resist work platform policy. The discussion presents implications for design for worker communication channels. The channels should be spaces with independent moderation and data protection-by-design that enable workers to safely build collective knowledge without fear of platform monitoring. Future work should follow implementations during transposition and test which governance and interface choices produce trust and capacity for collective action. Our contribution is to surface the governance dimension of worker communication and to translate these insights into design implications for future channels.

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future use cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.

Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

City governments in the United States are increasingly pressured to adopt emerging technologies. Yet, these systems often risk biased and disparate outcomes. Scholars studying public sector technology design have converged on the need to ground these systems in the goals and organizational contexts of employees using them. We expand our understanding of employees' contexts by focusing on the equity practices of city government employees to surface important equity considerations around public sector data and technology use. Through semi-structured interviews with thirty-six employees from ten departments of a U.S. city government, our findings reveal challenges employees face when operationalizing equity, perspectives on data needs for advancing equity goals, and the design space for acceptable government technology. We discuss what it looks like to foreground equity in data use and technology design, and considerations for how to support city government employees in operationalizing equity with and without official equity offices.

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems—communication content, modality, frequency, and direction—and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system’s workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.

The challenges of data collection in nonprofits for performance and funding reports are well-established in HCI research. Few studies, however, delve into improving the data collection process. Our study proposes ideas to improve data collection by exploring challenges that social workers experience when labeling their case notes. Through collaboration with an organization that provides intensive case management to those experiencing homelessness in the U.S., we conducted interviews with caseworkers and held design sessions where caseworkers, managers, and program analysts examined storyboarded ideas to improve data labeling. Our findings suggest several design ideas on how data labeling practices can be improved: Aligning labeling with caseworker goals, enabling shared control on data label design for a comprehensive portrayal of caseworker contributions, improving the synthesis of qualitative and quantitative data, and making labeling user-friendly. We contribute design implications for data labeling to better support multiple stakeholder goals in social service contexts.

Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated workers’ experiences under algorithmic management and how to design interventions, rarely are the perspectives of stakeholders who inform or craft policy sought. To bridge this, we propose using data probes—interactive visualizations of workers’ data that show the impact of technology practices on people—exploring them in 12 semi-structured interviews with policy informers, (driver-)organizers, litigators, and a lawmaker in the rideshare space. We show how data probes act as boundary objects to assist stakeholder interactions, demystify technology for policymakers, and support worker collective action. We discuss the potential for data probes as training tools for policymakers, and considerations around data access and worker risks when using data probes.

While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such NLP work due to poor alignment with fact-checker practices, values, and needs. To address this, we investigate a co-design method, Matchmaking for AI, to enable fact-checkers, designers, and NLP researchers to collaboratively identify what fact-checker needs should be addressed by technology, and to brainstorm ideas for potential solutions. Co-design sessions we conducted with 22 professional fact-checkers yielded a set of 11 design ideas that offer a "north star'', integrating fact-checker criteria into novel NLP design concepts. These concepts range from pre-bunking misinformation, efficient and personalized monitoring misinformation, proactively reducing fact-checker potential biases, and collaborative writing fact-check reports. Our work provides new insights into both human-centered fact-checking research and practice and AI co-design research.

With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations (IF-Plan). We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.

On-demand work platforms are attractive alternatives to traditional employment arrangements. However, several questions around employment classification, compensation, data privacy, and equitable outcomes remain open. The abilities of algorithmic management to structure different forms of platform-worker relationships compounds fraught regulatory debates. Understanding the conditions of algorithmic management that result in these variations could point us towards better worker futures. In this work, we studied the platform-worker relationships in Instacart work through the accounts of its workers. From a qualitative analysis of 400 Reddit posts by Instacart’s workers, we identified sources and types of ambiguity that gave rise to open-ended experiences for workers. Ambiguities supplemented gamification mechanisms to regulate worker behaviors. Yet, they also generated affective experiences for workers that enabled their playful participation in the Reddit community. We propose the frame of ludification to explain these seemingly contradicting findings and conclude with implications for accountability in on-demand work platforms.

In many creator economy platforms, algorithms significantly impact creators’ practices and decisions about their creative expression and monetization. Emerging research suggests that the opacity of the algorithm and platform policies often distract creators from their creative endeavors. To study how algorithmic platforms can be more ‘creator-friendly,’ we conducted a mixed-methods study: interviews (N=14) and a participatory design workshop (N=12) with YouTube creators. Through the interviews, we found how creators’ folk theories of the curation algorithm impact their work strategies — whether they choose to work with or against the algorithm — and the associated challenges in the process. In the workshop, creators explored solution ideas to overcome the aforementioned challenges, such as fostering diverse and creative expressions, achieving success as a creator, and motivating creators to continue their job. Based on these findings, we discuss design opportunities for how algorithmic platforms can support and motivate creators to sustain their creative work.

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers’ own data as well as city-level data, we create probes—interactive data visuals—that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers’ well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.

Abstract Governmental and organizational policy increasingly claims to be data‐driven, data‐informed, or knowledge‐driven. We explore the data practices of local governments and nonprofits a seeking to end homelessness in the City of Austin. Drawing on 31 interviews with stakeholders, alongside the reflections and experiences of our interdisciplinary, cross‐sector collaborative team, we consider the role of data in guiding and informing interventions and policy regarding homelessness. Ending homelessness is a particularly challenging scenario for intervention, with increasing politicization, changing circumstances, and needing rapid intervention to reduce harm. In exploring some implications of data science “in the wild” as it is deployed, understood, and supported within the Travis County Continuum of Care (CoC), we analyze how data‐intensive work connects and engages across disciplinary boundaries. Furthermore, we consider how data science and the iField can collaborate in addressing complex, social problems as advisors and partners with invested organizations.

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders---decision makers (faculty) and decision subjects (students)---use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.

Prior research has studied the detrimental impact of algorithmic management on gig workers and strategies that workers devise in response. However, little work has investigated alternative platform designs to promote worker well-being, particularly from workers’ own perspectives. We use a participatory design approach wherein workers explore their algorithmic imaginaries to co-design interventions that center their lived experiences, preferences, and well-being in algorithmic management. Our interview and participatory design sessions highlight how various design dimensions of algorithmic management, including information asymmetries and unfair, manipulative incentives, hurt worker well-being. Workers generate designs to address these issues while considering competing interests of the platforms, customers, and themselves, such as information translucency, incentives co-configured by workers and platforms, worker-centered data-driven insights for well-being, and collective driver data sharing. Our work offers a case study that responds to a call for designing worker-centered digital work and contributes to emerging literature on algorithmic work.

As robots become more ubiquitous across human spaces, it is becoming increasingly relevant for researchers to ask the question, “how can we ensure that we are designing robots to be sufficiently equipped to treat people fairly?”. This workshop brings together researchers across the fields of Human-Robot Interaction (HRI), fairness in machine learning, design, and transparency in AI to shed light on the relevant methodological challenges surrounding issues of fairness and transparency in HRI. In our workshop, we will attempt to identify synergies between these various fields. In particular, we will focus on how HRI can leverage these existing rich body of work to guide the formalization of fairness metrics and methodologies. Another goal of the workshop is to foster a community of interdisciplinary researchers to encourage collaboration. The complexity in defining fairness lies in its context sensitive nature, as such we look to the influx of definitions from the field of fairness in artificial intelligence, design, and organizational psychology to derive a set of definitions that could serve as guidelines for researchers in HRI.

Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has proposed the use of Markov Decision Processes (MDPs) to formulate decision-making with long-term fairness requirements in dynamically changing environments, and demonstrated major challenges in directly deploying heuristic and rule-based policies that worked well in static environments. We show that policy optimization methods from deep reinforcement learning can be used to find strictly better decision policies that can often achieve both higher overall utility and less violation of the fairness requirements, compared to previously-known strategies. In particular, we propose new methods for imposing fairness requirements in policy optimization by regularizing the advantage evaluation of different actions. Our proposed methods make it easy to impose fairness constraints without reward engineering or sacrificing training efficiency. We perform detailed analyses in three established case studies, including attention allocation in incident monitoring, bank loan approval, and vaccine distribution in population networks.

Abstract Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID‐19. We present findings from a cross‐sectional survey of 454 US adults—243 older (65+) and 211 younger (18–64) adults—who responded to questionnaires on human values, trust in COVID‐19 information sources, attention to information quality, self‐efficacy, and factual knowledge about COVID‐19. Path analysis showed that trust in direct personal contacts (B = 0.071, p = .04) and attention to information quality (B = 0.251, p < .001) were positively related to self‐efficacy for coping with COVID‐19. The human value of self‐transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self‐efficacy in coping with COVID‐19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001–0.104) and factual knowledge about COVID‐19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003–0.089). Our path model offers guidance for fine‐tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID‐19 and other public health crises.

Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social media, we conducted a 4 (label type: algorithm, community, third-party fact-checker, and no label) X 2 (post ideology: liberal vs. conservative) between-subjects online experiment (N = 1,677) in the context of COVID-19 health information. The results suggest that for liberal users, all labels reduced the perceived accuracy and believability of fake posts regardless of the posts' ideology. In contrast, for conservative users, the efficacy of the labels depended on whether the posts were ideologically consistent: algorithmic labels were more effective in reducing the perceived accuracy and believability of fake conservative posts compared to community labels, whereas all labels were effective in reducing their belief in liberal posts. Our results shed light on the differing effects of various misinformation labels dependent on people's political ideology.

Artificial intelligence is increasingly being used to manage the workforce. Algorithmic management promises organizational efficiency, but often undermines worker well-being. How can we computationally model worker well-being so that algorithmic management can be optimized for and assessed in terms of worker well-being? Toward this goal, we propose a participatory approach for worker well-being models. We first define worker well-being models: Work preference models---preferences about work and working conditions, and managerial fairness models---beliefs about fair resource allocation among multiple workers. We then propose elicitation methods to enable workers to build their own well-being models leveraging pairwise comparisons and ranking. As a case study, we evaluate our methods in the context of algorithmic work scheduling with 25 shift workers and 3 managers. The findings show that workers expressed idiosyncratic work preference models and more uniform managerial fairness models, and the elicitation methods helped workers discover their preferences and gave them a sense of empowerment. Our work provides a method and initial evidence for enabling participatory algorithmic management for worker well-being.

The use of AI-enabled hiring software raises questions about the practice of Human Resource (HR) professionals' use of the software and its consequences. We interviewed 15 recruiters and HR professionals about their experiences around two decision-making processes during hiring: sourcing and assessment. For both, AI-enabled software allowed the efficient processing of candidate data, thus providing the ability to introduce or advance candidates from broader and more diverse pools. For sourcing, it can serve as a useful learning resource to find candidates. Though, a lack of trust in data accuracy and an inadequate level of control over algorithmic candidate matches can create reluctance to embrace it. For assessment, its implementation varied across companies depending on the industry and the hiring scenario. Its inclusion may redefine HR professionals' job content as it automates or augments pieces of the existing hiring process. Finally, we discuss how candidate roles that recruiters and HR professionals support drive the use of algorithmic hiring software.

Emerging research suggests that people trust algorithmic decisions less than human decisions. However, different populations, particularly in marginalized communities, may have different levels of trust in human decision-makers. Do people who mistrust human decision-makers perceive human decisions to be more trustworthy and fairer than algorithmic decisions? Or do they trust algorithmic decisions as much as or more than human decisions? We examine the role of mistrust in human systems in people’s perceptions of algorithmic decisions. We focus on healthcare Artificial Intelligence (AI), group-based medical mistrust, and Black people in the United States. We conducted a between-subjects online experiment to examine people’s perceptions of skin cancer screening decisions made by an AI versus a human physician depending on their medical mistrust, and we conducted interviews to understand how to cultivate trust in healthcare AI. Our findings highlight that research around human experiences of AI should consider critical differences in social groups.

Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. We then extend our approach to settings with multiple human decision makers. We demonstrate the effectiveness of our proposed methods using both synthetic and real human responses, and find that our methods outperform both the algorithm and the human when they each make decisions on their own. We also show how personalized routing in the presence of multiple human decision-makers can further improve the human-machine team performance.

McCarthy (1998) defined Artificial Intelligence (AI) as both “the science and engineering of in- telligent machines, especially computer programs” and the “computational part of the ability to achi...

Abstract In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID‐19) epidemic, we (a) examine challenges associated with what we term “global information crises”; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short‐ and long‐term research, education, and practice in information science.

Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users' online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm's existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users' attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.

Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method – or voting rule – to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people’s true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count.

Real-time remote interaction has become easier and richer powered by recent advances in mobile computing and communication. A number of research have been explored on enriching family interaction by augmenting an interaction channel with asynchronous communication [6] or additional sensory stimuli [5]. However, it is still far from achieving a sense of living together for family members involuntarily living apart, especially in context-aware impromptu interaction. For families living together, it is trivial to naturally perceive behavioral and situational contexts of the other and initiate a relevant interaction intuitively. For example, a wife starts a casual chat with asking her husband what he is going to cook when she sees him going to the kitchen or hears a simmering sound.

As algorithms increasingly take managerial and governance roles, it is ever more important to build them to be perceived as fair and adopted by people. With this goal, we propose a procedural justice framework in algorithmic decision-making drawing from procedural justice theory, which lays out elements that promote a sense of fairness among users. As a case study, we built an interface that leveraged two key elements of the framework---transparency and outcome control---and evaluated it in the context of goods division. Our interface explained the algorithm's allocative fairness properties (standards clarity) and outcomes through an input-output matrix (outcome explanation), then allowed people to interactively adjust the algorithmic allocations as a group (outcome control). The findings from our within-subjects laboratory study suggest that standards clarity alone did not increase perceived fairness; outcome explanation had mixed effects, increasing or decreasing perceived fairness and reducing algorithmic accountability; and outcome control universally improved perceived fairness by allowing people to realize the inherent limitations of decisions and redistribute the goods to better fit their contexts, and by bringing human elements into final decision-making.

Algorithms increasingly govern societal functions, impacting multiple stakeholders and social groups. How can we design these algorithms to balance varying interests in a moral, legitimate way? As one answer to this question, we present WeBuildAI, a collective participatory framework that enables people to build algorithmic policy for their communities. The key idea of the framework is to enable stakeholders to construct a computational model that represents their views and to have those models vote on their behalf to create algorithmic policy. As a case study, we applied this framework to a matching algorithm that operates an on-demand food donation transportation service in order to adjudicate equity and efficiency trade-offs. The service's stakeholders--donors, volunteers, recipient organizations, and nonprofit employees--used the framework to design the algorithm through a series of studies in which we researched their experiences. Our findings suggest that the framework successfully enabled participants to build models that they felt confident represented their own beliefs. Participatory algorithm design also improved both procedural fairness and the distributive outcomes of the algorithm, raised participants' algorithmic awareness, and helped identify inconsistencies in human decision-making in the governing organization. Our work demonstrates the feasibility, potential and challenges of community involvement in algorithm design.

People in work-separated families have been heavily relying on cutting-edge face-to-face communication services. Despite their ease of use and ubiquitous availability, experiences in living together are still far incomparable to those through remote face-to-face communication. We envision that enabling a remote person to be spatially superposed in one's living space would be a breakthrough to catalyze pseudo living-together interactivity. We propose HomeMeld, a zero-hassle self-mobile robotic system serving as a co-present avatar to create a persistent illusion of living together for those who are involuntarily living apart. The key challenges are 1) continuous spatial mapping between two heterogeneous floor plans and 2) navigating the robotic avatar to reflect the other's presence in real time under the limited maneuverability of the robot. We devise a notion of functionally equivalent location and orientation to translate a person's presence into another in a heterogeneous floor plan. We also develop predictive path warping to seamlessly synchronize the presence of the other. We conducted extensive experiments and deployment studies with real participants.

No abstract available.

Algorithms are increasingly being incorporated into diverse services that orchestrate multiple stakeholders' needs and interests. How can we design these algorithmic services to make decisions that are not only efficient, but also fair and motivating? We take a human-centered approach to identify and address challenges in building human-centered algorithmic services. We are in the process of building an allocation algorithm for 412 Food Rescue, an organization that matches food donations with non-profit organizations. As part of this ongoing project, we conducted interviews with multiple stakeholders in the service-organization staff, donors, volunteers, recipient non-profits and their clients, and everyday citizens-in order to understand how the allocation algorithm, interfaces, and surrounding work practices should be designed. The findings suggest that we need to understand and account for varying fairness notions held by stakeholders; consider people, contexts, and interfaces for algorithms to work fairly in the real world; and preserve meaningfulness and social interaction in automation in order to build fair and motivating algorithmic services.

How do individuals perceive algorithmic vs. group-made decisions? We investigated people's perceptions of mathematically-proven fair division algorithms making social division decisions. In our first qualitative study, about one third of the participants perceived algorithmic decisions as less than fair (30% for self, 36% for group), often because algorithmic assumptions about users did not account for multiple concepts of fairness or social behaviors, and the process of quantifying preferences through interfaces was prone to error. In our second experiment, algorithmic decisions were perceived to be less fair than discussion-based decisions, dependent on participants' interpersonal power and computer programming knowledge. Our work suggests that for algorithmic mediation to be fair, algorithms and their interfaces should account for social and altruistic behaviors that may be difficult to define in mathematical terms.

Software algorithms are changing how people work in an ever-growing number of fields, managing distributed human workers at a large scale. In these work settings, human jobs are assigned, optimized, and evaluated through algorithms and tracked data. We explore the impact of this algorithmic, data-driven management on human workers and work practices in the context of Uber and Lyft, new ridesharing services. Our findings from a qualitative study describe how drivers responded when algorithms assigned work, provided informational support, and evaluated their performance, and how drivers used online forums to socially make sense of the algorithm features. Implications and future work are discussed.

Telepresence means business people can make deals in other countries, doctors can give remote medical advice, and soldiers can rescue someone from thousands of miles away. When interaction is mediated, people are removed from and lack context about the person they are making decisions about. In this paper, we explore the impact of technological mediation on risk and dehumanization in decision-making. We conducted a laboratory experiment involving medical treatment decisions. The results suggest that technological mediation influences decision making, but its influence depends on an individual's self-construal: participants who saw themselves as defined through their relationships (interdependent self-construal) recommended riskier and more painful treatments in video conferencing than when face-to-face. We discuss implications of our results for theory and future research.

Current approaches to personalization either presuppose people's needs and automatically tailor services or provide formulaic options for people to customize. We propose a complementary approach to personalization: a reflective strategy that helps people realize what matters to them and enables them to better personalize services themselves. To design this strategy, we first studied the practices of eight personal health service providers. We then tested the strategy's efficacy by building a Fitbit Plan website that encouraged Fitbit users to customize a plan or accept an automatically tailored plan. For one group of users, the website used the reflective strategy to assist in the plan setup process. A two-week between-subjects field experiment showed that the reflective strategy helped motivate users to carry out their plans, increasing their average daily steps by 2,425 steps. Without the reflective strategy, users either set easy goals or failed to carry out system-created plans, ultimately showing no change in their average daily steps. This work suggests that helping people reflect on and connect with their own goals in using a personalized service could advance the effectiveness of the service.

A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure.

Designing radically new technology systems that people will want to use is complex. Design teams must draw on knowledge related to people's current values and desires to envision a preferred yet plausible future. However, the introduction of new technology can shape people's values and practices, and what-we-know-now about them does not always translate to an effective guess of what the future could, or should, be. New products and systems typically exist outside of current understandings of technology and use paradigms; they often have few interaction and social conventions to guide the design process, making efforts to pursue them complex and risky. User Enactments (UEs) have been developed as a design approach that aids design teams in more successfully investigate radical alterations to technologies' roles, forms, and behaviors in uncharted design spaces. In this paper, we reflect on our repeated use of UE over the past five years to unpack lessons learned and further specify how and when to use it. We conclude with a reflection on how UE can function as a boundary object and implications for future work.

Prior research has investigated the effect of interactive social agents presented on computer screens or embodied in robots. Much of this research has been pursued in labs and brief field studies. Comparatively little is known about social agents embedded in the workplace, where employees have repeated interactions with the agent, alone and with others. We designed a social robot snack delivery service for a workplace, and evaluated the service over four months allowing each employee to use it for two months. We report on how employees responded to the robot and the service over repeated encounters. Employees attached different social roles to the robot beyond a delivery person as they incorporated the robot's visit into their workplace routines. Beyond one-on-one interaction, the robot created a ripple effect in the workplace, triggering new behaviors among employees, including politeness, protection of the robot, mimicry, social comparison, and even jealousy. We discuss the implications of these ripple effects for designing services incorporating social agents.

Creating and sustaining rapport between robots and people is critical for successful robotic services. As a first step towards this goal, we explored a personalization strategy with a snack delivery robot. We designed a social robotic snack delivery service, and, for half of the participants, personalized the service based on participants' service usage and interactions with the robot. The service ran for each participant for two months. We evaluated this strategy during a 4-month field experiment. The results show that, as compared with the social service alone, adding personalized service improved rapport, cooperation, and engagement with the robot during service encounters.

When performing physical collaboration tasks, like packing a picnic basket together, humans communicate strongly and often subtly via multiple channels like gaze, speech, gestures, movement and posture. Understanding and participating in this communication enables us to predict a physical action rather than react to it, producing seamless collaboration. In this paper, we automatically learn key discriminative features that predict the intent to handover an object using machine learning techniques. We train and test our algorithm on multi-channel vision and pose data collected from an extensive user study in an instrumented kitchen. Our algorithm outputs a tree of possibilities, automatically encoding various types of pre-handover communication. A surprising outcome is that mutual gaze and inter-personal distance, often cited as being key for interaction, were not key discriminative features. Finally, we discuss the immediate and future impact of this work for human-robot interaction.

Influence through information and feedback has been one of the main approaches of persuasive technology. We propose another approach based on behavioral economics research on decision-making. This approach involves designing the presentation and timing of choices to encourage people to make self-beneficial decisions. We applied three behavioral economics persuasion techniques - the default option strategy, the planning strategy, and the asymmetric choice strategy - to promote healthy snacking in the workplace. We tested the strategies in three experimental case studies using a human snack deliverer, a robot, and a snack ordering website. The default and the planning strategies were effective, but they worked differently depending on whether the participants had healthy dietary lifestyles or not. We discuss designs for persuasive technologies that apply behavioral economics.

As geographically distributed teams become increasingly common, there are more pressing demands for communication work practices and technologies that support distributed collaboration. One set of technologies that are emerging on the commercial market is mobile remote presence (MRP) systems, physically embodied videoconferencing systems that remote workers use to drive through a workplace, communicating with locals there. Our interviews, observations, and survey results from people, who had 2-18 months of MRP use, showed how remotely-controlled mobility enabled remote workers to live and work with local coworkers almost as if they were physically there. The MRP supported informal communications and connections between distributed coworkers. We also found that the mobile embodiment of the remote worker evoked orientations toward the MRP both as a person and as a machine, leading to formation of new usage norms among remote and local coworkers.

For robots to get integrated in daily tasks assisting humans, robot-human interactions will need to reach a level of fluency close to that of human-human interactions. In this paper we address the fluency of robot-human hand-overs. From an observational study with our robot HERB, we identify the key problems with a baseline hand-over action. We find that the failure to convey the intention of handing over causes delays in the transfer, while the lack of an intuitive signal to indicate timing of the hand-over causes early, unsuccessful attempts to take the object. We propose to address these problems with the use of spatial contrast, in the form of distinct hand-over poses, and temporal contrast, in the form of unambiguous transitions to the hand-over pose. We conduct a survey to identify distinct hand-over poses, and determine variables of the pose that have most communicative potential for the intent of handing over. We present an experiment that analyzes the effect of the two types of contrast on the fluency of hand-overs. We find that temporal contrast is particularly useful in improving fluency by eliminating early attempts of the human.

One goal of assistive robotics is to design interactive robots that can help disabled people with tasks such as fetching objects. When people do this task, they coordinate their movements closely with receivers. We investigated how a robot should fetch and give household objects to a person. To develop a model for the robot, we first studied trained dogs and person-to-person handoffs. Our findings suggest two models of handoff that differ in their predictability and adaptivity.

Previous research has shown that design features that support privacy are essential for new technologies looking to gain widespread adoption. As such, privacy-sensitive design will be important for the adoption of social robots, as they could introduce new types of privacy risks to users. In this paper, we report findings from our preliminary study on users' perceptions and attitudes toward privacy in human-robot interaction, based on interviews that we conducted about a workplace social robot.

Handing over objects to humans is an essential capability for assistive robots. While there are infinite ways to hand an object, robots should be able to choose the one that is best for the human. In this paper we focus on choosing the robot and object configuration at which the transfer of the object occurs, i.e. the hand-over configuration. We advocate the incorporation of user preferences in choosing hand-over configurations. We present a user study in which we collect data on human preferences and a human-robot interaction experiment in which we compare hand-over configurations learned from human examples against configurations planned using a kinematic model of the human. We find that the learned configurations are preferred in terms of several criteria, however planned configurations provide better reachability. Additionally, we find that humans prefer hand-overs with default orientations of objects and we identify several latent variables about the robot's arm that capture significant human preferences. These findings point towards planners that can generate not only optimal but also preferable hand-over configurations for novel objects.

The mental structures that people apply towards other people have been shown to influence the way people cooperate with others. These mental structures or schemas evoke behavioral scripts. In this paper, we explore two different scripts, receptionist and information kiosk, that we propose channeled visitors' interactions with an interactive robot. We analyzed visitors' typed verbal responses to a receptionist robot in a university building. Half of the visitors greeted the robot (e.g., "hello") prior to interacting with it. Greeting the robot significantly predicted a more social script: more relational conversational strategies such as sociable interaction and politeness, attention to the robot's narrated stories, self-disclosure, and less negative/rude behaviors. The findings suggest people's first words in interaction can predict their schematic orientation to an agent, making it possible to design agents that adapt to individuals during interaction. We propose designs for interactive computational agents that can elicit people's cooperation.

The rate of donations made by individuals is relatively low in Korea when compared to other developed countries. To address this problem, we propose the DONA, an urban donation motivating robot prototype. The robot roams around in a public space and solicits donation from passers-by by engaging them through a pet like interaction. In this paper, we present the prototype of the robot and our design process.

The rate of donations made by individuals is relatively low in Korea when compared to other developed countries. To address this problem, we propose the DONA, an urban donation motivating robot prototype. The robot roams around in a public space and solicits donation from passers-by by engaging them through a pet like interaction. In this paper, we present the prototype of the robot and our design process.

Robots that operate in the real world will make mistakes. Thus, those who design and build systems will need to understand how best to provide ways for robots to mitigate those mistakes. Building on diverse research literatures, we consider how to mitigate breakdowns in services provided by robots. Expectancy-setting strategies forewarn people of a robot's limitations so people will expect mistakes. Recovery strategies, including apologies, compensation, and options for the user, aim to reduce the negative consequence of breakdowns. We tested these strategies in an online scenario study with 317 participants. A breakdown in robotic service had severe impact on evaluations of the service and the robot, but forewarning and recovery strategies reduced the negative impact of the breakdown. People's orientation toward services influenced which recovery strategy worked best. Those with a relational orientation responded best to an apology; those with a utilitarian orientation responded best to compensation. We discuss robotic service design to mitigate service problems.

We present the design of the Snackbot, a robot that will deliver snacks in our university buildings. The robot is intended to provide a useful, continuing service and to serve as a research platform for long-term Human-Robot Interaction. Our design process, which occurred over 24 months, is documented as a contribution for others in HRI who may be developing social robots that offer services. We describe the phases of the design project, and the design decisions and tradeoffs that led to the current version of the robot.

Similarly, User models can be useful for improving dialogue management. In this paper we analyze human-robot dialogues that occur during uncontrolled interactions and estimate relations between the initial dialogue turns and patterns of discourse that are indicative of such user traits as persistence and politeness. The significant effects shown in this preliminary study suggest that initial dialogue turns may be useful in modeling a user's interaction style.