As AI and data systems often falter when encountering queer identities and knowledge, reinforcing existing oppressions, queer people have resisted such systems and their normalizing tendencies. This pictorial explores tactics of queering AI through a collaborative zine-making project (i.e. zineography) that challenges generative AI and data systems. We share how we workshopped and materialized queering tactics in zine spreads; analyzed these spreads according to materials, content, and tone; and visualized our analysis as thematic collages. We contribute: (1) tangible characteristics of queering AI and data systems (i.e. materials, tones, and aesthetics); and (2) design opportunities for using zineographies as a radical method for building and collectively sharing knowledge about a marginalized community, including recommendations for enacting queer zineographies. By materializing queering tactics through zine-making, we invite embodied, action-oriented critiques that question dominant techno-solutionist movements and trace queer possibilities outside of their normalizing narratives.

Over the past two decades, discussions of design patterns have turned from encouragement (what to do) toward discouragement (what to avoid). Termed dark, deceptive, or otherwise harmful, user experience (UX) patterns that serve to monetize engagement while reproducing and sedimenting structural inequities are prevalent, which calls for a shifting conversation around UX development and learning. This pictorial uses a visual case study of a childcare worker platform to help critically contextualize largely abstracted or universalizing UX patterns. Developing a form of critical documentation we call Care Layering, we show how approaching UX patterns as embodied and culturally-situated resources sheds light on both limitations and opportunities around gig work platform engagement. We end with a discussion of how Care Layering helps designers work towards greater accountability in UX design.

Care work is always already unequal. It involves looking after others’ physical, psychological, emotional, and developmental needs. Paid care work tends to be conducted in private spaces, lack regulation, and reproduce unequal dynamics between clients and workers. These conditions lead to porous boundaries, a permeability experienced by workers between care and work, professional and personal, and private and public (sectors and spheres). Drawing on interviews with 16 workers who find work using Care.com, we argue that the porous boundaries of care work are reified in new ways through the design and use of emerging digitally mediated matching platforms. This has particular impacts for ranking personhood, reducing worker safety, and increasing atomization. In contrast, we find benefits in the forum-like structure and visible, interactive conversations of other platforms used to access childcare work. We end with a discussion of porousness by design and the trouble of locating design within worker platforms.

Design cards have long played an important role in reflection-directing the designer's gaze toward unasked questions, hidden consequences, and new horizons for speculative futuring. But few questions have been asked about what motivates the design process and how deck designers see their role in inquiry and/or world building. This paper looks to the Tarot deck as one iconic example of such a process. Drawing on interviews with nine Tarot deck creators, we surface themes of Tarot as scaffolding modes of personal and collective growth, forms of carework, and pathways for different ways of knowing. We discuss expanding design inquiry methods for understanding and elevating forms of spiritual connection and care; and moving from anti-appropriation to ante-appropriation.

Over the past few years, AI bias has become a central concern within design and computing fields. But as the concept of bias has grown in visibility, its meaning and form have become harder to grasp. To help designers realize bias, we take inspiration from textile bias (the skew of woven material) and examine the topic across its myriad forms: visual, textual, and tactile. By introducing a slanted experience of material and therefore of reality, we explore the translation of fraught machine learning algorithms into personal and probing artifacts. In this pictorial, we present nine pieces that materialize complex relationships with machine learning; ground these relationships in the present and the personal; and point to generative ways of engaging with biased systems around us.

Explainability has become a dominant aspect of developing more accountable AI systems. But for AI to be more accountable, we as designers must also reflect on our own positioning—including aspects of ourselves that are difficult or impossible to fully explain, yet still influence our design processes. Just as designers develop explainable AI, we explore how AI can be used to develop the unexplainable designer through documenting our creation and use of a machine learning-generated tarot deck. Alongside design researchers, we consider the promise of such machine learning-generated artifacts for self-reflection on our creative and collaborative roles—and our responsibilities—when designing AI systems, and we discuss how the artifact sparked acts of inspiriting, a process of bringing the ineffable (back) into our engagements with machine learning systems.

It is difficult to design systems that honor the complex and often contradictory emotions that can be surfaced by sensitive encounters with recommender systems. To explore the design and ethical considerations in this space, we interviewed 20 people who had recently seen sensitive content through Facebook’s Memories feature. Interviewees typically described how (1) expectedness, (2) context of viewing, and (3) what we describe as “affective sense-making” were important factors for how they perceived “bittersweet” content, a sensitizing concept from our interviews that we expand upon. To address these user needs, we pose provocations to support critical work in this area and we suggest that researchers and designers: (1) draw inspiration from no/low-technology artifacts, (2) use empirical research to identify contextual features that have negative impacts on users, and (3) conduct user studies on affective sense-making.

The application of artificial intelligence (AI) to the behavioral health domain has led to a growing interest in the use of machine learning (ML) techniques to identify patterns in people's personal data with the goal of detecting-and even predicting-conditions such as depression, bipolar disorder, and schizophrenia. This paper investigates the data science practices and design narratives that underlie AI-mediated behavioral health through the situational analysis of three natural language processing (NLP) training datasets. Examining datasets as a sociotechnical system inextricably connected to particular social worlds, discourses, and infrastructural arrangements, we identify several misalignments between the technical project of dataset construction and benchmarking (a current focus of AI research in the behavioral health domain) and the social complexity of behavioral health. Our study contributes to a growing critical CSCW literature of AI systems by articulating the sensitizing concept ofdisordering datasets that aims to productively trouble dominant logics of AI/ML applications in behavioral health, and also support researchers and designers in reflecting on their roles and responsibilities working within this emerging and sensitive design space.

Corporations are increasingly empowering employees to hire on-demand workers via freelance platforms. We interviewed full-time employees of a global technology company who hired freelancers as part of their job responsibilities. While there has been prior work describing freelancers' perspectives there has been little research on those that hire them, the "clients", especially in the corporate context. We found that while freelance platforms reduce many administrative burdens, there are number of conditions in which using freelance platforms in a corporate context creates high transaction costs and power asymmetries that make it difficult for clients to negotiate work rights and responsibilities. This leads corporate employee clients to feel "stuck in the middle" between their employer, the platform, and the freelancer. Ultimately, these transactions costs are a potential barrier to wider adoption. If corporations want to leverage the value of the freelance economy then better guardrails, guidelines, and perhaps even creative technology solutions will be needed.

Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. What facial analysis technologies can do is determined by the data available to train and evaluate them with. In this study, we specifically focus on this data by examining how race and gender are defined and annotated in image databases used for facial analysis. We found that the majority of image databases rarely contain underlying source material for how those identities are defined. Further, when they are annotated with race and gender information, database authors rarely describe the process of annotation. Instead, classifications of race and gender are portrayed as insignificant, indisputable, and apolitical. We discuss the limitations of these approaches given the sociohistorical nature of race and gender. We posit that the lack of critical engagement with this nature renders databases opaque and less trustworthy. We conclude by encouraging database authors to address both the histories of classification inherently embedded into race and gender, as well as their positionality in embedding such classifications.

Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.

Sociotechnical imaginaries are futures that people envision might be possible and desirable. They have a real impact on how systems are designed and what values they have embedded in their design. This article examines imaginaries about autonomous systems, decentralized systems, and decentralized autonomous systems. Through a discussion of the literature on autonomous and decentralized systems and how these imaginaries play out in the blockchain community based on my qualitative research, I demonstrate how decentralized autonomous systems are related to imaginaries about the organization of and the future of work. I identify three framings of imaginaries about autonomous systems: (1) autonomous technology as physical objects, (2) as mathematical rules, and (3) as artificial mangers. I also identify two sometimes conflicting framings of imaginaries about distributed and decentralized technology: these technologies as a new form of production and as freedom from control. These imaginaries intersect in decentralized autonomous systems, and I examine what they can tell us about the design and governance of such technologies. Lastly, I suggest ways of using the concept of imaginaries in participatory design.

We present our experiences with an SMS-based system for providing transit information based solely on existing cellular and GPS networks. The aim is to permit the development of information services that do not rely on a central authority or complex web hosting. We developed and applied our system to the network of privately-run marshrutka buses in Bishkek, Kyrgyzstan. However, our goal is to more broadly address issues of ad-hoc shared transportation systems in the developing world. A custom designed GPS-GSM unit is placed on a vehicle, and users can query our server over SMS with their own non-GPS-enabled cell phones. We report on the accuracy of our location naming approach and estimates of bus arrival times. In addition, we summarize interviews with bus drivers and bus riders relating their views of the system and outline directions for future work. Our system is a grassroots solution to the persistent lack of transport information in developing countries.

This work consists of two main components: (a) a longitudinal ethnographic study in Kyrgyzstan that demonstrates the importance of transportation resources in the developing world and how to plan for an appropriate ICT solution, and (b) the results of a proof-of-concept system engineered to create a bottom-up, transportation information infrastructure using only GPS and SMS. Transportation is a very important shared resource; enabling efficient and effective use of such resources aids overall development goals.