This paper seeks to encourage the human-centered development of workplace technologies by demonstrating how care can inform the design of AI-based systems. The paper builds upon the ethics of care from feminist theory as well as from HCI and CSCW scholarship to foreground the interconnection between technology, those who use technology, and expressions of care in the workplace. Qualitative field research involving skilled trade workers and smart hand tool design provides evidence of how care can influence technology design and how socio-technical interventions can enable expressions of care related to safety, training, and effective tool use. The paper expands the feminist consideration of care to include skilled trade workers and showcases their potential role as co-designers of socio-technical interventions. The paper concludes by recommending further research that can expand upon these emergent concepts and lead to further collaborations that benefit socio-technical system design, and the evolution of AI related to the future of work in the skilled trades.

As the trend towards personalization in health science and technology fields has proliferated, researchers focusing on personal informatics (PI) tools for health management have grappled with when and how to design for a personalized user experience. While previous literature has addressed users’ perspectives on personalization, an important and underexplored research area is how personalization is approached within organizational contexts. This paper reports findings from qualitative interviews with designers, developers, and product managers at a multinational company that designs, builds, and commercializes PI tools for health management. The results point to four types of barriers participants consistently encountered, often leading personalization efforts to be scaled back, and three pragmatic approaches to personalization often adopted in the face of these barriers. The paper concludes with design implications for incorporating personalization while remaining aligned with the nuances, realities, and potential strain on systems and resources inherent to the design process in company settings.

The challenges of data collection in nonprofits for performance and funding reports are well-established in HCI research. Few studies, however, delve into improving the data collection process. Our study proposes ideas to improve data collection by exploring challenges that social workers experience when labeling their case notes. Through collaboration with an organization that provides intensive case management to those experiencing homelessness in the U.S., we conducted interviews with caseworkers and held design sessions where caseworkers, managers, and program analysts examined storyboarded ideas to improve data labeling. Our findings suggest several design ideas on how data labeling practices can be improved: Aligning labeling with caseworker goals, enabling shared control on data label design for a comprehensive portrayal of caseworker contributions, improving the synthesis of qualitative and quantitative data, and making labeling user-friendly. We contribute design implications for data labeling to better support multiple stakeholder goals in social service contexts.

Artificial Intelligence (AI) auditing is a relatively new area of work. Currently, there is a lack of uniform standards and regulation. As a result, the AI auditing ecosystem is very diverse, and AI auditing professionals use a variety of different auditing methods. So far, little is known about how AI auditors approach the concept of trust in AI through AI audits, in particular regarding the trust of users. This paper reports findings from interviews with 19 AI auditing stakeholders to understand how AI auditing professionals seek to create calibrated trust in AI tools and AI audits. Themes identified included the AI auditing ecosystem, participants' experiences with AI auditing, and trust in AI audits and AI. The paper adds to the existing research on trust in AI and trustworthiness in AI by adding perspectives of key stakeholders regarding trust in AI Audits by users as an essential and currently less explored part of the trust in AI research. This paper shows how information asymmetry in respect to AI audits can decrease the value of audits for users and consequently their trust in AI systems. Study participants suggest key elements for rebuilding trust and suggest recommendations for the AI auditing industry, such as monitoring of auditors and effective communication about AI audits.

This paper lays out an approach to co-designing Artificial Intelligence (AI)-enhanced smart hand tools with skilled trade workers employed at a local municipality. Skilled trade workers contribute to society by building the infrastructure upon which the public depends. In addition, these technical interventions offer an opportunity for workers to benefit from the data they generate via smart hand tools, potentially creating a new empowerment dynamic with employers. Therefore, we consider technologies that support skilled trade workers in performing their work effectively, safely, and with increased levels of autonomy to be considered Public Interest Technologies (PIT). Interdisciplinary research is underway that aligns these approaches with Public Interest Design (PID) principles which informs researchers' desire to explore how emerging technologies - data cooperatives, blockchain, smart contracts, and data dividends - can further smart hand tools' empowerment dynamic. Future participatory design efforts may deliver additional insights and further impact technology deployment.

Abstract We describe the design of a mixed-mode study that illustrates an adaptive approach to conducting HCI research. This mixed-mode approach is resilient to circumstances created by public health crises such as the COVID-19 pandemic. We based our study around a web-based survey instrument that was used both online (N = 205) and in an HCI lab (N = 29). Both modalities offer their respective advantages and limitations but complement each other to paint a picture that could not be obtained with either modality by itself. We believe that this approach may be timely and helpful for HCI researchers who moved to online data collection during the pandemic and are looking for ways to augment that data with lab data. This study design provides a potential template for mixed-mode research that can be resilient to future societal crises.

Abstract The scale and complexity of the data and algorithms used in artificial intelligence (AI)‐based systems present significant challenges for anticipating their ethical, legal, and policy implications. Given these challenges, who does the work of AI ethics, and how do they do it? This study reports findings from interviews with 26 stakeholders in AI research, law, and policy. The primary themes are that the work of AI ethics is structured by personal values and professional commitments, and that it involves situated meaning‐making through data and algorithms. Given the stakes involved, it is not enough to simply satisfy that AI will not behave unethically; rather, the work of AI ethics needs to be incentivized.

Abstract Governmental and organizational policy increasingly claims to be data‐driven, data‐informed, or knowledge‐driven. We explore the data practices of local governments and nonprofits a seeking to end homelessness in the City of Austin. Drawing on 31 interviews with stakeholders, alongside the reflections and experiences of our interdisciplinary, cross‐sector collaborative team, we consider the role of data in guiding and informing interventions and policy regarding homelessness. Ending homelessness is a particularly challenging scenario for intervention, with increasing politicization, changing circumstances, and needing rapid intervention to reduce harm. In exploring some implications of data science “in the wild” as it is deployed, understood, and supported within the Travis County Continuum of Care (CoC), we analyze how data‐intensive work connects and engages across disciplinary boundaries. Furthermore, we consider how data science and the iField can collaborate in addressing complex, social problems as advisors and partners with invested organizations.

Abstract Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID‐19. We present findings from a cross‐sectional survey of 454 US adults—243 older (65+) and 211 younger (18–64) adults—who responded to questionnaires on human values, trust in COVID‐19 information sources, attention to information quality, self‐efficacy, and factual knowledge about COVID‐19. Path analysis showed that trust in direct personal contacts (B = 0.071, p = .04) and attention to information quality (B = 0.251, p < .001) were positively related to self‐efficacy for coping with COVID‐19. The human value of self‐transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self‐efficacy in coping with COVID‐19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001–0.104) and factual knowledge about COVID‐19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003–0.089). Our path model offers guidance for fine‐tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID‐19 and other public health crises.

Image descriptions are how people who are blind or have low vision (BLV) access information depicted within images. To our knowledge, no prior work has examined how a description for an image should be designed for different scenarios in which users encounter images. Scenarios consist of the information goal the person has when seeking information from or about an image, paired with the source where the image is found. To address this gap, we interviewed 28 people who are BLV to learn how the scenario impacts what image content (information) should go into an image description. We offer our findings as a foundation for considering how to design next-generation image description technologies that can both (A) support a departure from one-size-fits-all image descriptions to context-aware descriptions, and (B) reveal what content to include in minimum viable descriptions for a large range of scenarios.

We present an empirical study into the visual content people who are blind consider to be private. We conduct a two-stage interview with 18 participants that identifies what they deem private in general and with respect to their use of services that describe their visual surroundings based on camera feeds from their personal devices. We then describe a taxonomy of private visual content that is reflective of our participants’ privacy-related concerns and values. We discuss how this taxonomy can benefit services that collect and sell visual data containing private information so such services are better aligned with their users.

Abstract In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID‐19) epidemic, we (a) examine challenges associated with what we term “global information crises”; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short‐ and long‐term research, education, and practice in information science.

AI image captioning challenges encourage broad participation in designing algorithms that automatically create captions for a variety of images and users. To create large datasets necessary for these challenges, researchers typically employ a shared crowdsourcing task design for image captioning. This paper discusses findings from our thematic analysis of 1,064 comments left by Amazon Mechanical Turk workers using this task design to create captions for images taken by people who are blind. Workers discussed difficulties in understanding how to complete this task, provided suggestions of how to improve the task, gave explanations or clarifications about their work, and described why they found this particular task rewarding or interesting. Our analysis provides insights both into this particular genre of task as well as broader considerations for how to employ crowdsourcing to generate large datasets for developing AI algorithms.

Information and communication technology (ICT) has increasingly important implications for our everyday lives, with the potential to both solve existing social problems and create new ones. This article focuses on one particular group of ICT professionals, computational modelers, and explores how these ICT professionals perceive their own societal responsibilities. Specifically, the article uses a mixed‐method approach to look at the role of professional codes of ethics and explores the relationship between modelers’ experiences with, and attitudes toward, codes of ethics and their values. Statistical analysis of survey data reveals a relationship between modelers’ values and their attitudes and experiences related to codes of ethics. Thematic analysis of interviews with a subset of survey participants identifies two key themes: that modelers should be faithful to the reality and values of users and that codes of ethics should be built from the bottom up. One important implication of the research is that those who value universalism and benevolence may have a particular duty to act on their values and advocate for, and work to develop, a code of ethics.

New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time‐series analysis. The results from two large‐scale experiments with 3.8 million full‐text articles and 48 million metadata records support the conclusion that full‐text features are significantly more useful for prediction than metadata‐only features and that the most accurate predictions result from combining the metadata and full‐text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full‐text features.

This paper describes a probabilistic latent variable model that is designed to detect human values such as justice or freedom that a writer has sought to reflect or appeal to when participating in a public debate. The proposed model treats the words in a sentence as having been chosen based on specific values; values reflected by each sentence are then estimated by aggregating values associated with each word. The model can determine the human values for the word in light of the influence of the previous word. This design choice was motivated by syntactic structures such as noun+noun, adjective+noun, and verb+adjective. The classifier based on the model was evaluated on a test collection containing 102 manually annotated documents focusing on one contentious political issue - Net neutrality, achieving the highest reported classification effectiveness for this task. We also compared our proposed classifier with human second annotator. As a result, the proposed classifier effectiveness is statistically comparable with human annotators.

Human values play an important role in shaping the design and use of information technologies. Research on values in social computing is challenged by disagreement about indicators and objects of study as researchers distribute their focus across contexts of technology design, adoption, and use. This paper draws upon a framework that clarifies how to see values in social computing research by describing values dimensions, comprised of sources and attributes of values in sociotechnical systems. This paper uses the framework to compare how diverse research methods employed in social computing surface values and make them visible to researchers. The framework provides a tool to analyze the strengths and weaknesses of each method for observing values dimensions. By detailing how and where researchers might observe interactions between values and technology design and use, we hope to enable researchers to systematically identify and investigate values in social computing.

We report on some initial findings of an investigation into current practices in, and the need for, information/computer ethics curricula at the graduate level. We give some results and analysis from a survey of faculty and graduate students at four diverse U.S. institutions. Faculty and students agree that students will face professional ethical challenges after graduation, but assessment of students' preparedness for these challenges differs widely across the surveyed institutions. A clear majority of faculty and students expressed support for an elective graduate-level ethics course, and roughly half supported a required graduate-level ethics course.

Net neutrality is the focus of an important policy debate that is tied to technological innovation, economic development, and information access. We examine the role of human values in shaping the Net neutrality debate through a content analysis of testimonies from U.S. Senate and FCC hearings on Net neutrality. The analysis is based on a coding scheme that we developed based on a pilot study in which we used the Schwartz Value Inventory. We find that the policy debate surrounding Net neutrality revolves primarily around differences in the frequency of expression of the values of innovation and wealth, such that the proponents of Net neutrality more frequently invoke innovation, while the opponents of Net neutrality more frequently invoke wealth in their prepared testimonies. The paper provides a novel approach for examining the Net neutrality debate and sheds light on the connection between information policy and research on human values.

Abstract Trust in information is developing into a vitally important topic as the Internet becomes increasingly ubiquitous within society. Although many discussions of trust in this environment focus on issues like security, technical reliability, or e‐commerce, few address the problem of trust in the information obtained from the Internet. The authors assert that there is a strong need for theoretical and empirical research on trust within the field of information science. As an initial step, the present study develops a model of trust in digital information by integrating the research on trust from the behavioral and social sciences with the research on information quality and human– computer interaction. The model positions trust as a key mediating variable between information quality and information usage, with important consequences for both the producers and consumers of digital information. The authors close by outlining important directions for future research on trust in information science and technology.

Abstract Information technology designers and users are generally treated as interacting yet distinct groups. Although approaches such as participatory design attempt to bring these groups together, such efforts are viewed as temporary and restricted to a specific knowledge domain where users can share key information and insights with designers. The author explores case studies that point to a different situation, role hybridization. Role hybridization focuses on the ability of individuals to shift from one knowledge domain to another, thus allowing for simultaneous membership within two otherwise distinct social worlds. While some studies focus on the ability of designers to act as users, this study focuses on the opposite situation, users who become designers. Interview and participant observation data is used to explore hybrid user–designers in two case studies: frog dissection simulations used in K‐12 biology education and human anatomy simulations used in medical education. Hybrid users as designers are one part of a larger design–use interface, illustrating the mutually constructive relationship between the activities of information technology design and use. Users as designers also challenge the traditional power relationship between designers and users, leading to a novel and exciting form of user‐centered design.