A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems.

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We present a qualitative study of 35 United States households whose occupants have made significant accommodations to their homes and behaviors in order to be more environmentally responsible. Our goal is to inform the design of future sustainable technologies through an exploration of existing "green" lifestyles. We describe the motivations, practices, and experiences of the participants. The participants had diverse motivations ranging from caring for the Earth to frugal minimalism, and most participants also evidenced a desire to be unique. Most participants actively and consciously managed their homes and their daily practices to optimize their environmental responsibility. Their efforts to be environmentally responsible typically required significant dedication of time, attention, and other resources. As this level of commitment and desire to be unique may not generalize readily to the broader population, we discuss the importance of interactive technologies that influence surrounding infrastructure and circumstances in order to facilitate environmental responsibility.

Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

Abstract

By its nature, the discipline of human computer interaction must take into consideration the issues that are most pertinent to humans. We believe that the CHI community faces an unanswered challenge in the creation of interactive systems: environmental sustainability. For example, climate scientists argue that the most serious consequences of climate change can be averted, but only if fundamental changes are made. The goal of this SIG is to raise awareness of these issues in the CHI community and to start a conversation about the possibilities and responsibilities we have to address issues of sustainability.

Abstract. As sensing technologies become increasingly distributed and democratized, citizens and novice users are becoming responsible for the kinds of data collection and analysis that have traditionally been the purview of professional scientists and analysts. Leveraging this citizen engagement effectively, however, requires not only tools for sensing and data collection but also mechanisms for understanding and utilizing input from both novice and expert stakeholders. When successful, this process can result in actionable findings that leverage and engage community members and build on their experiences and observations. We explored this process of knowledge production through several dozen interviews with novice community members, scientists, and regulators as part of the design of a mobile air quality monitoring system. From these interviews, we derived design principles and a framework for describing data collection and knowledge generation in citizen science settings, culminating in the user-centered design of a system for community analysis of air quality data. Unlike prior systems, ours breaks analysis tasks into discrete mini-applications designed to facilitate and scaffold novice contributions. An evaluation we conducted with community members in an area with air quality concerns indicates that these mini-applications help participants identify relevant phenomena and generate local knowledge contributions.

We introduce a technique for creating novel, enhanced thumbnails of Web pages. These thumbnails combine the advantages of plain thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. As expected, there was a strong effect of question category. For some questions, text summaries outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text summaries. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) had more consistent performance than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.