huashen218/convxai-cia-dataset huggingface dataset:
Title: Opening Research Commissioning To Civic Participation: Creating A Community Panel To Review The Social Impact of HCI Research Proposals
Authors: family: G Johnson
given: Ian; family: Crivellaro
given: Clara
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Social justice HCI, participation, HCI research, design, commissioning

Abstract: In this paper we report insights from the design and delivery of a process that invited distinct groups of citizens to co-develop and apply social impact assessment criteria for the purpose of reviewing research proposals on HCI, social justice and digital technologies. We describe our process, designed to create dialogic spaces that foster critical engagements with technologies and social issues, cooperation and peer-support. In our findings we explore how people defined and contextualised social impact in lived experiences, negotiated and legitimised their role as reviewers, and articulated the value of HCI research for social justice. We reflect on the significance of involving citizens in the commissioning of research that addresses inequalities and social justice in technology design and draw implications for HCI researchers concerned with ethical dimensions of technology. The work contributes to HCI and civic engagement's traditions to develop effective participatory methods and collaborative processes to produce digital technologies that support social justice.
Title: AI in Global Health: The View from the Front Lines
Authors: family: Ismail
given: Azra; family: Kumar
given: Neha
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Social Good, AI, Qualitative, HCI4D, India, Healthcare

Abstract: There has been growing interest in the application of AI for Social Good, motivated by scarce and unequal resources globally. We focus on the case of AI in frontline health, a Social Good domain that is increasingly a topic of significant attention. We offer a thematic discourse analysis of scientific and grey literature to identify prominent applications of AI in frontline health, motivations driving this work, stakeholders involved, and levels of engagement with the local context. We then uncover design considerations for these systems, drawing from data from three years of ethnographic fieldwork with women frontline health workers and women from marginalized communities in Delhi (India). Finally, we outline an agenda for AI systems that target Social Good, drawing from literature on HCI4D, post-development critique, and transnational feminist theory. Our paper thus offers a critical and ethnographic perspective to inform the design of AI systems that target social impact.
Title: A Critical Examination of Virtual Reality Technology in the Context of the Minority Body
Authors: family: Gerling
given: Kathrin; family: Spiel
given: Katta
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Surrogate Body Theory, Disability Studies

Abstract: Virtual Reality (VR) holds the promise of immersing people in virtual worlds. However, initial work on the relationship between VR and disability suggests that VR is a body-centric technology that poses barriers for disabled users. We supplement this work with a theoretical analysis of immersive VR through the lens of Surrogate Body theory, a concept from media theory for the structured examination of interactive media in use. Leveraging Critical Disability Studies, particularly the theory of the Minority Body, we explore the assumptions about bodies inherent in VR, and we reflect on implications of these assumptions when disabled people engage with the technology. Our findings show that VR is an inherently ableist technology that assumes a ‘corporeal standard’ (i.e., an ‘ideal’, non-disabled human body), and fails to adequately accommodate disabled people. We conclude with implications for HCI research on VR, and discuss design approaches that foster inclusive technology development.
Title: User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics
Authors: family: Crisan
given: Anamaria; family: Correll
given: Michael
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: topic modelling, unsupervised clustering, text analytics, human-in-the-loop ML

Abstract: Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a “human-in-the-loop” approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness, and opacity of these models means even minor changes could induce large and potentially undesirable changes in resulting model. In this paper we conduct a simulation-based analysis of human-centered interactions with topic models, with the objective of measuring the sensitivity of topic models to common classes of user actions. We find that user interactions have impacts that differ in magnitude but often negatively affect the quality of the resulting modelling in a way that can be difficult for the user to evaluate. We suggest the incorporation of sensitivity and “multiverse” analyses to topic model interfaces to surface and overcome these deficiencies. Code and Data Availability: https://osf.io/zgqaw
Title: Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
Authors: family: Crisan
given: Anamaria; family: Fiore-Gartland
given: Brittany
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Data Science, Machine Learning, Automation, Data Scientists

Abstract: AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals from organizations of different sizes to characterize how they currently use, or intend to use, AutoML systems in their data science work. Our investigation also captures how data visualization is used in conjunction with AutoML systems. Our findings identify three usage scenarios for AutoML that resulted in a framework summarizing the level of automation desired by data workers with different levels of expertise. We surfaced the tension between speed and human oversight and found that data visualization can do a poor job balancing the two. Our findings have implications for the design and implementation of human-in-the-loop visual analytics approaches.
Title: Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations
Authors: family: Zehrung
given: Rachael; family: Singhal
given: Astha; family: Correll
given: Michael; family: Battle
given: Leilani
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: algorithmic trust, recommendation source, Visualization recommendation systems, automation

Abstract: More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender’s ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users.
Title: Causal Perception in Question-Answering Systems
Authors: family: Law
given: Po-Ming; family: Lo
given: Leo Yu-Ho; family: Endert
given: Alex; family: Stasko
given: John; family: Qu
given: Huamin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: question answering, correlation and causation

Abstract: Root cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing different information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the findings, we propose ways to reduce the illusion of causality when using question-answering systems.
Title: Understanding Narrative Linearity for Telling Expressive Time-Oriented Stories
Authors: family: Lan
given: Xingyu; family: Xu
given: Xinyue; family: Cao
given: Nan
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Storytelling, Narrative Order, Narrative Visualization

Abstract: Creating expressive narrative visualization often requires choosing a well-planned narrative order that invites the audience in. The narrative can either follow the linear order of story events (chronology), or deviate from linearity (anachronies). While evidence exists that anachronies in novels and films can enhance story expressiveness, little is known about how they can be incorporated into narrative visualization. To bridge this gap, this work introduces the idea of narrative linearity to visualization and investigates how different narrative orders affect the expressiveness of time-oriented stories. First, we conducted preliminary interviews with seven experts to clarify the motivations and challenges of manipulating narrative linearity in time-oriented stories. Then, we analyzed a corpus of 80 time-oriented stories and identified six most salient patterns of narrative orders. Next, we conducted a crowdsourcing study with 221 participants. Results indicated that anachronies have the potential to make time-oriented stories more expressive without hindering comprehensibility.
Title: Understanding Data Accessibility for People with Intellectual and Developmental Disabilities
Authors: family: Wu
given: Keke; family: Petersen
given: Emma; family: Ahmad
given: Tahmina; family: Burlinson
given: David; family: Tanis
given: Shea; family: Szafir
given: Danielle Albers
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: graphical perception & cognition, human-subjects quantitative studies

Abstract: Using visualization requires people to read abstract visual imagery, estimate statistics, and retain information. However, people with Intellectual and Developmental Disabilities (IDD) often process information differently, which may complicate connecting abstract visual information to real-world quantities. This population has traditionally been excluded from visualization design, and often has limited access to data related to their well being. We explore how visualizations may better serve this population. We identify three visualization design elements that may improve data accessibility: chart type, chart embellishment, and data continuity. We evaluate these elements with populations both with and without IDD, measuring accuracy and efficiency in a web-based online experiment with time series and proportion data. Our study identifies performance patterns and subjective preferences for people with IDD when reading common visualizations. These findings suggest possible solutions that may break the cognitive barriers caused by conventional design guidelines.
Title: Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online
Authors: family: Lee
given: Crystal; family: Yang
given: Tanya; family: Inchoco
given: Gabrielle D; family: Jones
given: Graham M.; family: Satyanarayan
given: Arvind
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Twitter, data visualization, Facebook, data literacy, network analysis, digital ethnography

Abstract: Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government’s pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.
Title: Mapping the Landscape of COVID-19 Crisis Visualizations
Authors: family: Zhang
given: Yixuan; family: Sun
given: Yifan; family: Padilla
given: Lace; family: Barua
given: Sumit; family: Bertini
given: Enrico; family: Parker
given: Andrea G
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: COVID-19, crisis informatics, Visualization

Abstract: In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people’s attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.
Title: Does Interaction Improve Bayesian Reasoning with Visualization?
Authors: family: Mosca
given: Ab; family: Ottley
given: Alvitta; family: Chang
given: Remco
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Data Analysis, Human-Subjects Quantitative Studies, Reasoning, Interaction Design, Problem Solving, Decision Making

Abstract: Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed for evaluating whether allowing users to interact with a static visualization can improve their reasoning. Through two crowdsourced studies, we show that adding interaction to a static Bayesian reasoning visualization does not improve participants’ accuracy on a Bayesian reasoning task. In some cases, it can significantly detract from it. Moreover, we demonstrate that underlying visualization design modulates performance and that people with high versus low spatial ability respond differently to different interaction techniques and underlying base visualizations. Our work suggests that interaction is not as unambiguously good as we often believe; a well designed static visualization can be as, if not more, effective than an interactive one.
Title: Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts
Authors: family: Kim
given: Dae Hyun; family: Setlur
given: Vidya; family: Agrawala
given: Maneesh
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visually prominent features, Captions, takeaways., line charts

Abstract: Charts often contain visually prominent features that draw attention to aspects of the data and include text captions that emphasize aspects of the data. Through a crowdsourced study, we explore how readers gather takeaways when considering charts and captions together. We first ask participants to mark visually prominent regions in a set of line charts. We then generate text captions based on the prominent features and ask participants to report their takeaways after observing chart-caption pairs. We find that when both the chart and caption describe a high-prominence feature, readers treat the doubly emphasized high-prominence feature as the takeaway; when the caption describes a low-prominence chart feature, readers rely on the chart and report a higher-prominence feature as the takeaway. We also find that external information that provides context, helps further convey the caption’s message to the reader. We use these findings to provide guidelines for authoring effective chart-caption pairs.
Title: Can Anthropographics Promote Prosociality?A Review and Large-Sample Study
Authors: family: Morais
given: Luiz; family: Jansen
given: Yvonne; family: Andrade
given: Nazareno; family: Dragicevic
given: Pierre
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: information visualization, infographics, empathy, compassion., anthropographics

Abstract: Visualizations designed to make readers compassionate with the persons whose data is represented have been called anthropographics and are commonly employed by practitioners. Empirical studies have recently examined whether anthropographics indeed promote empathy, compassion, or the likelihood of prosocial behavior, but findings have been inconclusive so far. This work contributes a detailed overview of past experiments, and two new experiments that use large samples and a combination of design strategies to maximize the possibility of finding an effect. We tested an information-rich anthropographic against a simple bar chart, asking participants to allocate hypothetical money in a crowdsourcing study. We found that the anthropographic had, at best, a small effect on money allocation. Such a small effect may be relevant for large-scale donation campaigns, but the large sample sizes required to observe an effect and the noise involved in measuring it make it very difficult to study in more depth. Data and code are available at https://osf.io/xqae2/.
Title: The Public Life of Data: Investigating Reactions to Visualizations on Reddit
Authors: family: Kauer
given: Tobias; family: Dörk
given: Marian; family: Ridley
given: Arran L.; family: Bach
given: Benjamin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: personal data, data visualization, reddit

Abstract: This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people’s reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
Title: Interpreting the Effect of Embellishment on Chart Visualizations
Authors: family: Andry
given: Tiffany; family: Hurter
given: Christophe; family: Lambotte
given: François; family: Fastrez
given: Pierre; family: Telea
given: Alexandru
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: text tagging, datasets, gaze detection, neural networks

Abstract: Infographics range from minimalism that aims to convey the raw data to elaborately decorated, or embellished, graphics that aim to engage readers by telling a story. Several studies have shown evidence to negative, but also positive, effects on embellishments. We conducted a set of experiments to gauge more precisely how embellishments affect how people relate to infographics and make sense of the conveyed story. By analyzing questionnaires, interviews, and eye-tracking data simplified by bundling, we show that, within bounds, embellishments have a positive effect on how users get engaged in understanding an infographic, with very limited downside. To our knowledge, our work is the first that fuses the aforementioned three information sources to understand infographics. Our findings can help to design more fine-grained studies to quantify the effects of embellishments and also to design infographics that effectively use the embellishments’ positive aspects identified. I think the contribution does not appear well in the abstract. It’s not just that the visual embellishments are positive. We show a methodology that allows us to see what these effects are (in addition to engagement, memorization and recall) at several levels (scales, interviews, eye tracking) which are therefore physiological and emotional. We could include this idea in the abstract?