Recently, there has been growing interest in designing future technologies for and with Students of Color (SoC) within HCI scholarship. This study focuses on centering SoC's desire for flourishing as a means of empowerment and prioritizing their positive and meaningful experiences within predominantly white institutions (PWIs) in the United States. Motivated by this, we conducted a two-phase case study with SoC. In the first phase, qualitative semi-structured interviews with 17 SoC were conducted. Findings revealed the development of learned values such as pride, resistance, hope, and aspirations, which facilitated students' strong personal growth and cultural development. In the second phase, we utilized these findings to create a SoC learned values toolkit. This toolkit was used to facilitate five co-design studies with 13 SoC, to imagine future technologies that foster and promote their flourishing. The co-design research resulted in 13 social technology design proposals. Empirical analysis of the co-design sessions demonstrated that participants' desires involved finding safe spaces, accessing resources, connecting with racially similar communities, and building relationships with role models. We further identified an emergent tension between the envisioned technologies for flourishing and the ethical implications of trust, vulnerability, and the spread of misinformation. Resisting design universalism, this paper celebrates the specificity of a design process for our audience of SoC PWI students. We conclude by providing recommendations for future pluriversal designs that prioritize SoC's desire for flourishing.

Machine Learning (ML) is now integrated from everyday technologies to sophisticated infrastructures, providing fast, efficient, and scalable decision-making services, with increasing evidence of ML perpetuating invisible harms and biases. The hidden and socio-technical nature of ML biases can make them difficult to detect and prevent without proper literacy. To investigate this, we developed a novel online multiplayer board game Equality Engine, where players learn about various ML biases and debiasing techniques. We conducted a mixed-method formative playtest case study to solicit feedback from post-secondary students (N = 12) with a range of ML experience. We found that students' self-reported ML bias-debias knowledge improved significantly after playing the game. The game was perceived as easy to use because of the social interaction and immersion the game enabled. Students would also use the game in the future because of the self-reported knowledge gained from the game. Although these positive results may be influenced by measurement bias, our study contributes to the design of an approachable game, which not only facilitates exposure, collaboration, and opportunities for critical reflection on ML biases but also provides recommendations for future game designs that can facilitate ML ethics discourse and literacy among a broader audience.

Research and design in human-computer interaction centers problem-solving, causing a downstream effect of framing work with and for marginalized communities predominantly from the lens of deficit and damage. Frameworks such as assets-based design and designing for resilience have emerged to highlight the strengths of these communities. However, we observe an additional need to center positive aspects of humanity, such as joy, pleasure, rest, and cultural heritage, particularly for Black, Indigenous, and People of Color. In this paper, we present three case studies of existing technologies that center BIPOC flourishing to provide an alternative path for HCI. We propose six actionable tenets for designing for flourishing in the everyday: (1) seek opportunities to design for self-actualization, (2) identify and center the desire to flourish, (3) collaborate and distribute power, (4) refrain from damage-centered reductionism, (5) bring the humanity of a community into the design process, (6) and evaluate for self-sustainability.

Homelessness presents a long-standing problem worldwide. Like other welfare services, homeless services have gained increased traction in Machine Learning (ML) research. Unhoused persons are vulnerable and using their data in the ML pipeline raises serious concerns about the unintended harms and consequences of prioritizing different ML values. To address this, we conducted a critical analysis of 40 research papers identified through a systematic literature review in ML homelessness service provision research. We found that the values of novelty, performance, and identifying limitations were uplifted in these papers, whereas (in)efficiency, (low/high) cost, fast, (violated) privacy, and (homeless condition) reproducibility valuescollapse. Consequently, unhoused persons were lost (i.e., humans were deprioritized) at multi-level ML abstraction of predictors, categories, and algorithms. Our findings illuminate potential pathways forward at the intersection of data science, HCI and STS by situating humans at the center to support this vulnerable community.

The rise of automated text processing systems has led to the development of tools designed for a wide variety of application domains. These technologies are often developed to support non-technical users such as domain experts and are often developed in isolation of the tools primary user. While such developments are exciting, less attention has been paid to domain experts’ expectations about the values embedded in these automated systems. As a step toward addressing that gap, we examined values expectations of journalists and legal experts. Both these domains involve extensive text processing and place high importance on values in professional practice. We engaged participants from two non-profit organizations in two separate co-speculation design workshops centered around several speculative automated text processing systems. This study makes three interrelated contributions. First, we provide a detailed investigation of domain experts’ values expectations around future NLP systems. Second, the speculative design fiction concepts, which we specifically crafted for these investigative journalists and legal experts, illuminated a series of tensions around the technical implementation details of automation. Third, our findings highlight the utility of design fiction in eliciting not-to-design implications, not only about automated NLP but also about technology more broadly. Overall, our study findings provide groundwork for the inclusion of domain experts values whose expertise lies outside of the field of computing into the design of automated NLP systems.

Investigative data journalists work with a variety of data sources to tell a story. Though prior work has indicated that there is a close relationship between journalists' data work practices and that of data scientists. However, these relationships and data work practices are not empirically examined, and understanding them is crucial to inform the design of tools that are used by different groups of people including data scientists and data journalists. Thus, to bridge this gap, we studied investigative reporters' data work practices with one non-profit investigative newsroom. Our study design includes two activities: 1) semi-structured interviews with journalists, and 2) a sketching activity allowing journalists to depict examples of their work practices. By analyzing these data and synthesizing them across related prior work, we propose the major phases in the data-driven investigative journalism story idea generation process. Our study findings show that the journalists employ a collection of multiple, iterative, cyclic processes to identify journalistically "interesting'' story ideas. These processes both significantly resemble and show subtle nuanced differences with data science work practices identified in prior research. We further verified our proposal through a member check with key informants. This work offers three primary contributions. First, it provides a close glimpse into the main phases of investigative journalists' data-driven story idea generation technique. Second, it complements prior work studying formal data science practices by examining data-driven investigative journalists, whose primary expertise lies outside computing. Third, it identifies particular points in the data exploration processes that would benefit from design interventions and suggests future research directions.