Like humans, today's systems, such as robots and voice assistants, can express curiosity to learn and engage with their surroundings. While curiosity is a well-established human trait that enhances social connections and drives learning, no existing scales assess the perceived curiosity of systems. Thus, we introduce the Perceived System Curiosity (PSC) scale to determine how users perceive curious systems. We followed a standardized process of developing and validating scales, resulting in a validated 12-item scale with 3 individual sub-scales measuring explorative, investigative, and social dimensions of system curiosity. In total, we generated 831 items based on literature and recruited 414 participants for item selection and 320 additional participants for scale validation. Our results show that the PSC scale has inter-item reliability and convergent and construct validity. Thus, this scale provides an instrument to explore how perceived curiosity influences interactions with technical systems systematically.
https://dl.acm.org/doi/10.1145/3706598.3713087
Language is more than communication; it is a form of power. Whereas science has been scrutinized for privileging Western values and norms, what has been less explored is scientific linguistic performance (e.g. writing). The enforcement of English as the “normative standard” has prioritized hegemonic values and assumptions, thereby shaping the expectations of scientific performance. HCI/CSCW is dominated by heteropatriarchal Western practices, overlooking entangled values and assumptions impacting non-Western colleagues. Our work presents a design fiction (fictitious case study) envisioning a research contribution which embodies non-Western linguistic nuances as an alternative “normative standard” for scientific communication. Through this work, not only are we championing care in developing responsible linguistic practices in HCI/CSCW, but also epistemically challenging readers with intentional confusion. We establish a call to action for acknowledging and embracing different writing practices that are more inclusive of the diverse representation of scholars in HCI/CSCW.
https://dl.acm.org/doi/10.1145/3706598.3714073
The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development–e.g., through surveys, experiments, and interviews—with synthetic research data generated by large language models (LLMs). We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift. Initially skeptical, researchers were surprised to see similar narratives emerge in the LLM-generated data when using the interview probe. However, over several conversational turns, they went on to identify fundamental limitations, such as how LLMs foreclose participants’ consent and agency, produce responses lacking in palpability and contextual depth, and risk delegitimizing qualitative research methods. We argue that the use of LLMs as proxies for participants enacts the surrogate effect, raising ethical and epistemological concerns that extend beyond the technical limitations of current models to the core of whether LLMs fit within qualitative ways of knowing.
https://dl.acm.org/doi/10.1145/3706598.3713220
HCI is future-oriented by nature: it explores new human--technology interactions and applies the findings to promote and shape vital visions of society. Still, the visions of futures in HCI publications seem largely implicit, techno-deterministic, narrow, and lacking in roadmaps and attention to uncertainties. A literature review centered on this problem examined futuring and its forms in the ACM Digital Library's most frequently cited HCI publications. This analysis entailed developing the four-category framework SPIN, informed by futures studies literature. The results confirm that, while technology indeed drives futuring in HCI, a growing body of HCI research is coming to challenge techno-centric visions. Emerging foci of HCI futuring demonstrate active exploration of uncertainty, a focus on human experience, and contestation of dominant narratives. The paper concludes with insight illuminating factors behind techno-centrism's continued dominance of HCI discourse, as grounding for five opportunities for the field to expand its contribution to futures and anticipation research.
https://dl.acm.org/doi/10.1145/3706598.3713759
The relationship between the Nigerian police and citizens is strained, hindering the co-design of conventional technologies to enhance community policing (CP) initiatives, hence the imperative to involve both in the design of a usable CP technology that can carter for their needs. Our preliminary findings indicate that Nigerian citizens are reluctant to participate in co-design activities with the police due to discomfort, which could potentially bias the design outcomes. Designing a CP technology with such stakeholders is crucial, but a new challenge for the Human Computer Interaction (HCI) community, as no existing framework has addressed it. We introduce Conflict Sensitive Design (CSD), a co-design approach that leverages mediation techniques (tension reduction, leveling, common ground reminder, separated meetings, formalizing agreements) to iteratively collect, analyze, and reconcile design inputs, ensuring that the final design is usable for CP enhancement. Our case application worked in CP technology requirements gathering with Nigerian CP stakeholders, and it could be extended to related HCI contexts. We present a structured approach to conflict resolution in co-design processes, and discuss the lessons learned as a spotlight to guide other designers in related contexts.
https://dl.acm.org/doi/10.1145/3706598.3714168
The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows. This leads to confusion about what assumptions are and what needs to be done with them. We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions. Through semi structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded. Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.
https://dl.acm.org/doi/10.1145/3706598.3713958
Embedded systems and interactive devices form an essential interface between the physical and digital world and are understandably an important focus for the HCI research community. However, scaling an interactive prototype of a new device concept to enable effective evaluation or to support the transition to a production-ready device is incredibly challenging. To better understand the issues innovators face when scaling up interactive device prototypes we report the results from 22 interviews with practitioners in the interactive device field, including eight academics involved in the HCI and manufacturing research communities. In our two-phase analysis we identify and validate the following four recurring themes. First and foremost is the observation that ``creating relationships with industry'' is hard. Second, ``effective communication requires a lot of effort'' despite the availability of modern collaboration tools. Thirdly, we observed that ``understanding the manufacturer's perspective'' can be difficult. Finally, ``prototyping is nothing like production''---the vast difference between these two activities still surprises many. Additionally, our university-based participants gave us further insights and helped us to identify challenges specific to the academic context, pointing to a number of opportunities relating to hardware device scaling.
https://dl.acm.org/doi/10.1145/3706598.3713214