Attending to Screens and Screenness: Guest editorial for special issue of Encounters

This special issue is a follow up on a PhD course entitled “Framing Screens: Knowledge, Interaction and Practice” held at the IT University of Copenhagen in the fall of 2010. Course participants from a variety of disciplines and countries joined the senior faculty who served as midwives for the births of new understandings and conceptualizations of screens

Pulling Back the Curtain on the Wizards of Oz

The Wizard of Oz method is an increasingly common practice in HCI and CSCW studies as part of iterative design processes for interactive systems. Instead of designing a fully-fledged system, the ‘technical work’ of key system components is completed by human operators yet presented to study participants as if computed by a machine. However, little is known about how Wizard of Oz studies are interactionally and collaboratively achieved in situ by researchers and participants. By adopting an ethnomethodological perspective, we analyse our use of the method in studies with a voice-controlled vacuum robot and two researchers present. We present data that reveals how such studies are organised and presented to participants and unpack the coordinated orchestration work that unfolds ‘behind the scenes’ to complete the study. We examine how the researchers attend to participant requests and technical breakdowns, and discuss the performative, collaborative, and methodological nature of their work. We conclude by offering insights from our application of the approach to others in the HCI and CSCW communities for using the method

I’m deleting as fast as I can: Negotiating learning practices in cyberspace

Learning in and through work is one of the many spaces in which pedagogy may unfold. Web technologies amplify this fluidity and online learning now encompasses a plethora of practices. In this paper I focus on the delete button and deleting practices of self-employed workers engaged in informal work-related learning in online communities. How the relational and material aspects of online pedagogical practices are being negotiated is explored. While deleting appears to be an everyday practice, understanding the delete button as a fluid object in fluid space begins to illuminate its complexity and multiple enactments. Deleting practices which work to stem the tide of information pushing itself onto screens, as well as those practices that attempt to delete traces left behind on screens and ‘in the cloud’, are examined. Actor Network Theory provides the theoretical and conceptual tools for this exploration. I conclude with observations on the politics of the delete button and implications for more sophisticated digital fluency in everyday pedagogy

From Creating Spaces for Civic Discourse to Creating Resources for Action

In this paper, we investigate the role of technology to address the concerns of a civil society group carrying out community-level consultation on the allocation of £1 million of community funds. We explore issues of devolved decision-making through the evaluation of a sociodigital system designed to foster deliberative virtues. We describe the ways in which this group used our system in their consultation practices. Our findings highlight how they adopted our technology to privilege specific forms of expression, ascertain issues in their community, make use of and make sense of community data, and create resources for action within their existing practices. Based on related fieldwork we discuss the impacts of structuring and configuring tools for ‘talk-based’ consultation in order to turn attention to the potential pitfalls and prospects for designing civic technologies that create resources for action for civil society

Crowdsourcing the Perception of Machine Teaching

Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.Comment: 10 pages, 8 figures, 5 tables, CHI2020 conferenc

Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems

Scholarship on algorithms has drawn on the analogy between algorithmic systems and bureaucracies to diagnose shortcomings in algorithmic decision-making. We extend the analogy further by drawing on Michel Crozier’s theory of bureaucratic organizations to analyze the relationship between algorithmic and human decision-making power. We present algorithms as analogous to impartial bureaucratic rules for controlling action, and argue that discretionary decision-making power in algorithmic systems accumulates at locations where uncertainty about the operation of algorithms persists. This key point of our essay connects with Alkhatib and Bernstein’s theory of ’street-level algorithms’, and highlights that the role of human discretion in algorithmic systems is to accommodate uncertain situations which inflexible algorithms cannot handle. We conclude by discussing how the analysis and design of algorithmic systems could seek to identify and cultivate important sources of uncertainty, to enable the human discretionary work that enhances systemic resilience in the face of algorithmic errors.Peer reviewe

Interdependence in Action: People with Visual Impairments and their Guides Co-constituting Common Spaces

Prior work on AI-enabled assistive technology (AT) for people with visual impairments (VI) has treated navigation largely as an independent activity. Consequently, much effort has focused on providing individual users with wayfinding details about the environment, including information on distances, proximity, obstacles, and landmarks. However, independence is also achieved by people with VI through interacting with others, such as in collaboration with sighted guides. Drawing on the concept of interdependence, this research presents a systematic analysis of sighted guiding partnerships. Using interaction analysis as our primary mode of data analysis, we conducted an empirical, qualitative study with 4 couples, each made up of person with a vision impairment and their sighted guide. Our results show how pairs used interactional resources such as turn-taking and body movements to both co-constitute a common space for navigation, and repair moments of rupture to this space. This work is used to present an exemplary case of interdependence and draws out implications for designing AI-enabled AT that shifts the emphasis away from independent navigation, and towards the carefully coordinated actions between people navigating togethe

Bottom-Up Organizing with Tools from On High: Understanding the Data Practices of Labor Organizers

This paper provides insight into the use of data tools in the American labor movement by analyzing the practices of staff employed by unions to organize alongside union members. We interviewed 23 field-level staff organizers about how they use data tools to evaluate membership. We find that organizers work around and outside of these tools to develop access to data for union members and calibrate data representations to meet local needs. Organizers mediate between local and central versions of the data, and draw on their contextual knowledge to challenge campaign strategy. We argue that networked data tools can compound field organizers' lack of discretion, making it more difficult for unions to assess and act on the will of union membership. We show how the use of networked data tools can lead to less accurate data, and discuss how bottom-up approaches to data gathering can support more accurate membership assessments