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Participatory Design scholars and practitioners have embraced speculative design approaches to challenge normative assumptions about sociotechnical futures and address the systemic lack of racial and class diversity in futuring. This paper draws upon a community-based participatory speculative design (PSD) project conducted with a group of working-class Detroiters, focusing on speculating about alternative community economies. We illustrate how PSD served as a process of ongoing “contamination” where the boundaries of community members’ visions of desired futures are opened up, troubled, and negotiated on the individual, alliance, and collective levels, thus forming new commons for collaboration and resistance across differences. For them, such contamination was a reflexive process aimed at identifying whose visions were excluded from their own and how community-held sociotechnical imaginary could emerge through collaboration. We argue that foregrounding contamination in PSD makes meaningful space for fostering reflexivity in knowledge production, while destabilizing and reassembling more inclusive sociotechnical futures. 

In Detroit, the largest Black-majority city in the United States, municipal authorities have deployed an array of surveillance technologies with the promise of containing crime and improving community safety. This article draws from a cross-sectional survey of over two thousand Detroit residents and multi-year community-based fieldwork in Detroit’s Eastside to examine local perceptions of policing surveillance technologies. Our survey reveals that respondents, notably those in more vulnerable positions, report higher perceived safety levels with policing surveillance cameras in their neighborhoods. However, when triangulating these results with insights from our fieldwork, we argue that these survey findings should not be taken as public support for surveillance. Alongside this seeming buy-in is a widely shared “better than nothing” imaginary among residents from impacted communities. “Better than nothing,” for the residents, is a pragmatic compromise and maneuver between being aware of the inherent flaws of surveillance technologies and settling for any available resource or hope. This notion of “better than nothing” unveils residents’ prolonged wait for digital justice and institutional accountability, which we show is where racialized infrastructural harm and exploitation are enacted along the temporal dimension. Our findings offer practical insights for counter-surveillance advocacy efforts. 

Economic crises such as the global recession and financial crisis of 2007 and 2008 and the Coronavirus disease (COVID-19) pandemic, have elevated new forms of economic cooperation. Supporting efforts in finding alternatives to capitalism requires understanding the role of design in imagining alternative economic futures and reaching those most harmed by current capitalistic models. Through a collaboration between a community organization in Detroit and a team of university researchers, we hosted and facilitated a five-week workshop series with Black and Brown working-class Detroiters where they collectively imagined alternative economic futures using speculative design. They proposed Community Capitalism, Childcare Collectives, and Village-Based Childcare as alternative economy concepts from the workshops and described their unique characteristics and traits of love, care, and inclusion. Aligning with generative justice frameworks, Detroiters prioritized sustainable families and communities. We contribute an understanding of technology’s role in the imagined economic futures, a discussion of what this means for community-involved governance, and a push for centering Afrofuturism in speculative design approaches to foster futures literacy. 

Concentric tube robots (CTRs) consist of a set of telescoping, pre-curved tubes, whose overall shape can be actively controlled by translating and rotating the tubes with respect to each other. The majority of CTRs to date consist of piecewise constant-curvature tubes, with a straight section followed by a single constant-curvature section. Several approaches have been proposed for CTR designs that can lead to improvements in metrics such as the workspace, orientability, dexterity, and stability. Here we propose to use CTRs with multiple constant-curvature sections. We perform two simulation studies that compare the performance of the multiple constant- curvature CTRs with standard single constant-curvature tubes. We also demonstrate how using one of the proposed multiple constant-curvature designs can enable the reduction in the number of tubes needed to achieve the same performance as a standard three-tube CTR. 

Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with consent and containing 83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of environments. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary. Through our generalizable baselines, we show that training supervised machine learning classifiers with our dataset achieves competitive performance on metrics relevant for dictionary retrieval, with 63% accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who are not present in the training or validation sets. 

Noticing differently commits to stepping out of familiar reference frameworks while attending to oft-neglected actors, relations, and ways of knowing for design. Photovoice is an arts- and community-based participatory approach allowing individuals to communicate their lives and stories about pressing community concerns through photography. This paper bridges photovoice and the commitment to noticing in HCI and design through a photovoice project with Detroit residents on safety and surveillance. The photovoice process—alongside the production, reflection, and dissemination of photographs—makes residents’ everyday situations legible and sensible, allowing both community members and researchers to orient to and engage with multiple viewpoints, sensibilities, and temporal trajectories. This process confronts the invisibility of both the sociotechnical infrastructures (in our case, surveillance infrastructures) and minoritized communities’ relational ontologies. By advocating participatory noticing in design research, we show the opportunities for adopting arts- and community-based participatory approaches in decentering dominant ways of knowing and seeing, while at the same time fostering community capacity and relations for future potentialities. 

Participatory action research (PAR) approaches center community members’ lived experiences and can spur positive change around pressing challenges faced by communities. Even though PAR and similar approaches have been increasingly adopted in HCI research that focuses on social justice and community empowerment, public-facing events that are based on this research and center community members’ voices are less common. This case study sheds light on how to initiate and organize events that build on existing PAR efforts, and what practical challenges might exist in this process. Building on a photovoice research project, we—a collaborative team of university researchers and staff members of a community organization in Eastside Detroit—co-organized a community-based public-facing exhibition that featured community members’ photographic narratives of personal and communal safety and surveillance. In this case study, we reflect on the challenges we experienced in planning and holding the exhibition. We contribute a set of practical guidelines to help researchers facilitate community-based events when conducting participatory action research in HCI. 

Safety has been used to justify the expansion of today’s large-scale surveillance infrastructures in American cities. Our work offers empirical and theoretical groundings on why and how the safety-surveillance conflation that reproduces harm toward communities of color must be denaturalized. In a photovoice study conducted in collaboration with a Detroit community organization and a university team, we invited 11 Black mid-aged and senior Detroiters to use photography to capture their lived experiences of navigating personal and community safety. Their photographic narratives unveil acts of “everyday noticing” in negotiating and maintaining their intricate and interdependent relations with human, non-human animals, plants, spaces, and material things, through which a multiplicity of meaning and senses of safety are produced and achieved. Everyday noticing, as simultaneously a survival skill and a more-than-human care act, is situated in residents’ lived materialities, while also serving as a site for critiquing the reductive and exclusionary vision embedded in large-scale surveillance infrastructures. By proposing an epistemological shift from surveillance-as-safety to safety-through-noticing, we invite future HCI work to attend to the fluid and relational forms of safety that emerge from local entanglement and sensibilities. 

Abstract Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies. 

Today, teachers have been increasingly relying on data-driven technologies to track and monitor student behavior data for classroom management. Drawing insights from interviews with 20 K--8 teachers, in this paper we unpack how teachers enacted both care and control through their data work in collecting, interpreting, and using student behavior data. In this process, teachers found themselves subject to surveilling gazes from parents, school administrators, and students. As a result, teachers had to manipulate the student behavior data to navigate the balance between presenting a professional image to surveillants and enacting care/control that they deemed appropriate. In this paper we locate two nuanced forms of teachers' data work that have been under-studied in CSCW: (1) data work as recontextualizing meanings and (2) data work as resisting surveillance. We discuss teachers' struggle over (in)visibility and their negotiation of autonomy and subjectivity in these two forms of data work. We highlight the importance of foregrounding and making space for informal data workers' (in our case, teachers') resistance and negotiation of autonomy in light of datafication.