How Data Workers Shape Datasets: The Role of Positionality in Data Collection and Annotation for Computer VisionData workers play a key role in the big data industry. Clients hire data workers to collect and annotate data with human identity concepts, like demographic categories or clothing items. Often, such workers are treated as computational—they are expected to quickly and objectively conduct their work, with the goal of having unbiased datasets for training and evaluating models. Computer vision is especially interested in fair and impartial data due to biases and unethical practices in the field. However, far from impartial, data workers imbue computer vision data with "biases" beyond correct versus incorrect answers. Data workers embed their own specific positional perspectives about identity concepts in both collection and annotation processes. Through interviews and ethnographic observations of data workers (both freelance and business process outsourcing (BPO) employees), we show how worker positionality influences decisions during data work. We also show how unintended outcomes, generally portrayed as "biases," occur when positionality is not explicitly considered in client instructions. We discuss how employing a lens of positionality in data work reveals the gulfs between data worker perspectives and client expectations, which are colored by a web of positional actors beyond isolated data workers. We propose positional (il)legibility as an approach to data work that embraces the reality of positionality in classification practices that the lens of "bias" fails to appropriately account for.2025MSMorgan Klaus Scheuerman et al.The Gig EconomyCSCW
“Moment to Moment”: A View From the Front Lines with Computing Ethics Teaching AssistantsThe HCI research community has long centered ethics in HCI research and practice. This interest has persisted as scholars highlight the need for more situated understandings and deeper integration of ethics into HCI. In parallel, HCI scholars and students have become increasingly involved in teaching computing ethics across many different university contexts, bringing in valuable perspectives informed by the connections between HCI and the socio-technical subject matter of computing ethics. Yet explicitly bringing these two threads together – examining the teaching of ethics through an HCI research lens – remains nascent. This paper integrates work in HCI and computing education to focus on the role and experience of computing ethics teaching assistants (CETAs), who are increasingly involved in ethics instruction and whose perspectives are predominantly missing in existing literature spanning HCI and computing education. Drawing on HCI theories and methods, our qualitative study of eleven CETAs at two American universities makes three contributions to the HCI literature. First, we build an understanding of who these TAs are with respect to the unique position of teaching computing ethics. Second, we characterize how CETAs’ teaching and learning is situated and shaped within different communities and institutional contexts. Finally, we sug- gest several implications for the design of ethics instruction within undergraduate computing programs. More broadly, our work can be viewed as a call to action, encouraging HCI scholars to play a more significant role in studying and designing the teaching and learning of computing ethics.2023CZCass Zegura et al.unaffiliatedTechnology Ethics & Critical HCIParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Collectives and Their Artifact EcologiesComputing today happens across multiple devices, applications, users, organizational units, and in the rest of the world outside. Groups and communities come together for different reasons and operate within contexts that may differ from dominant modes of production and consumption. With a foundation in activity theoretical HCI we develop the concept of collective artifact ecologies. This concept enables us to identify struggles of collective use of computational devices today, delimiting collective artifact ecologies in order to study and explain how they develop and overlap. Through an analysis of three empirical cases, we illustrate the notion of collectives and how they face challenges in establishing, maintaining and negotiating their artifact ecologies. This paper, therefore, contributes a theoretical foundation for analyzing groups and communities as collectives, with a particular emphasis on the multiple tools and artifacts they use. To serve as a starting point for further engagement with these concepts, we have provided a number of guiding questions to support the understanding of collective artifact ecologies.2022HKHenrik Korsgaard et al.Team Collaboration; Team CollaborationCSCW
“Self-Quaranteens” process COVID-19: Understanding information visualization language in memesThe COVID-19 pandemic has led to a surge of information visualizations that aim to increase our scientific understanding and communicate about the ongoing health crisis with the general public. In this time, there has also been significant use of data visualization language in artefacts from online communities that provide commentary on the pandemic and create meaning through participatory digital culture. Using a qualitative approach, this paper examines over 300 memes collected from a public social media group targeted to young adults in the United States that uses the language of data visualization to discuss topics related to COVID-19. We outline four main ways that data visualization language is used in these memes—as a coarse indicator, as a visual analogy, as an opportunity for augmentation with emotion or interpretation, and as a visual pun—as well as two ways that memes leverage traditional and emerging approaches in the information visualization community. We describe the context in which these memes are socially created and interpreted in light of the political nature of online spaces and connect this work to ongoing research on participation, emotion, and embodiment in information visualization. These results aim to start a conversation about the use of data visualization language in digital culture and more casual networked environments beyond official channels.2022LPLaura J Perovich et al.Pandemic Life; Pandemic LifeCSCW
Next Steps for Human-Computer IntegrationHuman-Computer Integration (HInt) is an emerging paradigm in which computational and human systems are closely interwoven. Integrating computers with the human body is not new. however, we believe that with rapid technological advancements, increasing real-world deployments, and growing ethical and societal implications, it is critical to identify an agenda for future research. We present a set of challenges for HInt research, formulated over the course of a five-day workshop consisting of 29 experts who have designed, deployed and studied HInt systems. This agenda aims to guide researchers in a structured way towards a more coordinated and conscientious future of human-computer integration.2020FMFlorian Floyd Mueller et al.Monash UniversityBrain-Computer Interface (BCI) & NeurofeedbackTechnology Ethics & Critical HCIUser Research Methods (Interviews, Surveys, Observation)CHI
Race, Gender and Beauty: The Effect of Information Provision on Online Hiring BiasesWe conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.2020WLWeiwen Leung et al.UnaffiliatedAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasInclusive DesignCHI
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, CreationWith the rise of big data, there has been an increasing need for practitioners in this space and an increasing opportunity for researchers to understand their workflows and design new tools to improve it. Data science is often described as data-driven, comprising unambiguous data and proceeding through regularized steps of analysis. However, this view focuses more on abstract processes, pipelines, and workflows, and less on how data science workers engage with the data. In this paper, we build on the work of other CSCW and HCI researchers in describing the ways that scientists, scholars, engineers, and others work with their data, through analyses of interviews with 21 data science professionals. We set five approaches to data along a dimension of interventions: Data as given; as captured; as curated; as designed; and as created. Data science workers develop an intuitive sense of their data and processes, and actively shape their data. We propose new ways to apply these interventions analytically, to make sense of the complex activities around data practices.2019MMMichael Muller et al.IBM ResearchInteractive Data VisualizationComputational Methods in HCICHI
"Privacy is not a concept, but a way of dealing with life:" Localization of Transnational Technology Platforms and Liminal Privacy Practices in CambodiaPrivacy scholarship has shown how norms of appropriate information flow and information regulatoryprocesses vary according to environment [7,42], which change as the environment changes, includingthrough the introduction of new technologies [44]. This paper describes findings from a qualitative researchstudy that examines practices and perceptions of privacy in Cambodia as the population rapidly moves into anonline environment (specifically Facebook, the most popular Internet tool in Cambodia today). We empiricallydemonstrate how the concept of privacy differs across cultures and show how the Facebook platform, asit becomes popular worldwide, catalyzes change in norms of information regulation. We discuss how thelocalization of transnational technology platforms provides a key site in which to investigate changing culturalideas about privacy, and to discover misalignments between different expectations for information flow. Finally,we explore ways that insufficient localization effort by transnational technology companies puts some of themost marginalized users at disproportionate information disclosure risk when using new Internet tools, andoffer some pragmatic suggestions for how such companies could improve privacy tools for users who are far -geographically or culturally - from where the tools are designed.2019MJMargaret C Jack et al.Privacy and TrustCSCW