When is ML data good?: Valuing in Public Health Datafication
説明

Data-driven approaches that form the foundation of advancements in machine learning (ML) are powered in large part by human infrastructures that enable the collection of large datasets. We study the movement of data through multiple stages of data processing in the context of public health in India, examining the data work performed by frontline health workers, data stewards, and ML developers. We conducted interviews with these stakeholders to understand their varied perspectives on valuing data across stages, working with data to attain this value, and challenges arising throughout. We discuss the tensions in valuing and how they might be addressed, as we emphasize the need for improved transparency and accountability when data are transformed from one stage of processing to the next.

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Forgetting Practices in the Data Sciences
説明

HCI engages with data science through many topics and themes. Researchers have addressed biased dataset problems, arguing that bad data can cause innocent software to produce bad outcomes. But what if our software is not so innocent? What if the human decisions that shape our data-processing software, inadvertently contribute their own sources of bias? And what if our data-work technology causes us to forget those decisions and operations? Based in feminisms and critical computing, we analyze forgetting practices in data work practices. We describe diverse beneficial and harmful motivations for forgetting. We contribute: (1) a taxonomy of data silences in data work, which we use to analyze how data workers forget, erase, and unknow aspects of data; (2) a detailed analysis of forgetting practices in machine learning; and (3) an analytic vocabulary for future work in remembering, forgetting, and erasing in HCI and the data sciences

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Unmaking as Agonism: Using Participatory Design with Youth to Surface Difference in an Intergenerational Urban Context
説明

Design has been used to contest existing socio-technical arrangements, provoke conversations around matters of concern, and operationalize radical theories such as agonism, which embraces difference and contention. However, the focus is usually on creating something new: a product, interface or artifact. In this paper, we investigate what happens when critical unmaking is deployed as a deliberate design strategy in an intergenerational, agonistic urban context. Specifically, we report on how youth in a six-week design internship used unmaking as a design move to subvert conventional narratives about their surrounding urban context. We analyze how this led to conflictual encounters at the local senior center, and compare it to the other, making-centric proposals which received favorable feedback but failed to raise the same important discussions. Through this ethnographic account, we argue that critical unmaking is important yet overlooked, and should be in the repertoire of design moves available for agonism and provocation.

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Insights and Opportunities for HCI Research into Hurricane Risk Communication
説明

Communicating risk to the public in the lead-up to tropical storms has the potential to significantly reduce the impacts on both livelihood and property. While significant research has been conducted in the storm risk community on how people receive, seek, and utilize risk information, given the importance of computing technologies and social media in these activities, human-centered design stands to make important contributions to this area. Drawing on an extensive literature review and 48 interviews with hurricane experts and members of the public, this paper makes three contributions. First, we provide a broad overview of hurricane risk communication. We then offer a set of guiding insights to inform HCI research work in this domain. Finally, we identify 6 opportunities that future human centered design work might pursue. In sum, this paper offers an invitation and a starting point for HCI to take up the problem of hurricane risk communication.

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Consumption Experiences in the Research Process
説明

Data collection is often a laborious enterprise that forms part of the wider craft skill of doing research. In this essay, I try to understand whether parts of research processes in Human-Centred Computing (HCC) have been commodified, with a particular focus on data collection. If data collection has been commodified, do researchers act as producers or consumers in the process? And if researchers are consumers, has data collection become a consumption experience? If so, what are the implications of this? I explore these questions by considering the status of craft and consumption in the research process and by developing examples of consumption experiences. I note the benefits of commodity research artefacts, while highlighting the potentially deleterious effects consumption experiences could have on our ability to generate insights into the relations between people and technology. I finish the paper by relating consumption experiences to contemporary issues in HCC and lay out a programme of empirical work that would help answer some of the questions this paper raises.

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