Unveiling Emotions: Attitudes Toward Affective Technology

With its ability to sense and/or generate human emotions, affective computing calls for a new generation of technology. This study brings affective technologies into focus which can sense human emotions. Compared to other types of technology, affective technologies have distinct characteristics—anthropomorphism, uncontrollability, capturing of highly sensitive data, unfamiliarity, and complexity—with fundamental effects on the interaction with humans. These characteristics of affective technology create a feeling of uncertainty about how such a system works. However, the attitudes people exhibit toward the usage, notably trust, such as affective assistance systems has received only scant attention. Hence, we define attitudes toward affective technology and contribute to the literature by proposing a research model that we analyzed using a quantitative methodology with 303 participants. From the theoretical model, we derive implications for theory, practice, and design

AiLingo – A Design Science Approach to Advancing Non-Expert Adults’ AI Literacy

Non-experts struggle in human-AI collaboration due to AI’s differences from more traditional technologies, such as inscrutability. Meanwhile, information systems research on AI education primarily focuses on students in formal learning settings and neglects non-expert adults. Applying a design science research approach, we develop a learning application (“AiLingo”) as an informal learning experience to advance non-expert adults’ AI literacy. Based on self-determination theory, we deduct design principles and features tailored to non-expert adults. Through experimental evaluation (n = 101), we find that a learning experience with our design features present (vs. absent) leads to greater AI literacy advancement. Additionally, we find downstream effects of AI literacy, as it increases AI usage continuance intention and leads to a more positive attitude toward AI. Our study contributes to AI literacy and educational literature with a perspective on non-expert adults, novel design knowledge for AI education, and the discovery of crucial AI literacy consequences

Toward a Science of Sustainability

This report presents an overview of research horizons in sustainability science. Its motivation is to help harness science and technology to foster a transition toward sustainability – toward patterns of development that promote human well-being while conserving the life-support systems of the planet. It builds on but does not explicitly address the vast range of relevant sector-specific and cross-sectoral problem-solving work now underway in fields ranging from green technologies in energy and manufacturing to urban design to agriculture and natural resources. It focuses on the narrower but essential task of characterizing the needs for fundamental work on the core concepts, methods, models, and measurements that, if successful, would support work across all of those sectoral applications by advancing fundamental understanding of the science of sustainability. The report sets forth the workshop’s findings and recommendations on six fundamental questions now facing scholars seeking to harness science and technology to foster sustainability: 1. What are the principal tradeoffs between human well-being and the natural environment, and how are those tradeoffs mediated by the ways in which people use nature? 2. What determines the adaptability of coupled human-environment systems and, more broadly, their vulnerability and robustness/resilience in the face of external shocks and internal dynamics? 3. What shapes the long term trends and transitions that set the stage on which humanenvironment interactions are played out? 4. How can theory and models be formulated that better account for the variation in types or trends of human-environment interactions? 5. How can society most effectively guide or manage human-environment systems toward a sustainability transition? 6. How can the “sustainability” of alternative trajectories of human-environment interactions be usefully and rigorously evaluated

Halo Effect Contamination in Assessments of Web Interface Design

This paper relies on findings and theory from both the human-computer interaction and cognitive psychology literatures in order to inquire into the extent to which the halo effect contaminates web interface design assessments. As a human cognitive bias, the halo effect manifests itself when a judge's evaluations of an entity's individual characteristics are negatively or positively distorted by the judge's overall affect toward the entity being judged. These distortions and halo-induced delusions have substantial negative implications for rational decisionmaking and the ability to objectively evaluate businesses, technologies, or other humans, and should hence be a critical consideration for both managers and organizations alike. Here we inquire into the halo effect using a controlled, randomized experiment involving more than 1,200 research subjects. Subjects' preexisting affective states were activated using polarizing issues including abortion rights, immigration policy, and gun control laws. Subjects were then asked to evaluate specific interface characteristics of six different types of websites, the textual content of which either supported or contradicted their preexisting affective beliefs. Comparing subject responses to objective control evaluations revealed strong evidence of halo effect contamination in assessments of web interface design, particularly among men. In light of the results, a theoretical framework integrating elements from cognitive and evolutionary psychology is proposed to explain the origins and purpose of the halo effect

Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis

This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening ontrol structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems

Assessment of Human Performance in Industry 5.0 Research Via Eye-Tracking and Cognitive Biases

Manufacturing assembly is combining previously made components or subassemblies into a final finished product. The assembly process can be manual, hybrid, or fully automated. Human operators who are involved in assembly use their judgment to perform the process. They collaborate with the other work agents such as assembly machines, robots, smart technologies, and computer interfaces. The recent Industrial revolution, Industry 5.0, exploits human expertise in collaboration with efficient and accurate machines. Manufacturing facilities that feature Industry 5.0 work settings require higher expectations, higher accuracy, sustainability solutions, mass customization of products, more human involvement, and digital technologies in smart workstations. Given these features, the cognitive load exerted on human workers in this environment is continuously increasing, leading to the use of cognitive heuristics. Cognitive biases are getting more attention in the cognitive ergonomics field, to help understand the operational behavior of workers. Manufacturing facilities can integrate cognitive assistance systems to work in parallel with physical and sensorial assistance systems. Cognitive assistance systems help toward better work conditions for workers and better overall system performance. This research explores the impact of human thinking style and using a cognitive assistance system on workers\u27 cognitive load, bias-related human performance, and user satisfaction. This research presents the design and experimental implementation of a research framework based on a well-established three-layer model for implementing Industry 5.0 in manufacturing. The research framework was designed to apply the dual-system theory and cognitive assistance in Assembly 5.0. Two experiments are presented to show the effectiveness of the proposed research framework. A cognitive assistance system was designed and compared to a benchmark system from LEGO ® Company. Subjective and objective measures were used to assess the thinking style, cognitive load, bias-related human performance, and user satisfaction in Assembly 5.0. As Industry 5.0 requires higher expectations, higher accuracy, smart workstations, and higher complexity, cognitive assistance systems can reduce the cognitive load and maintain the work efficiency and user satisfaction. Therefore, this work is important to industry to expand the use of cognitive ergonomic tools and employ them for A5.0 workers\u27 benefits

Assessment of Human Performance in Industry 5.0 Research Via Eye-Tracking and Cognitive Biases

Manufacturing assembly is combining previously made components or subassemblies into a final finished product. The assembly process can be manual, hybrid, or fully automated. Human operators who are involved in assembly use their judgment to perform the process. They collaborate with the other work agents such as assembly machines, robots, smart technologies, and computer interfaces. The recent Industrial revolution, Industry 5.0, exploits human expertise in collaboration with efficient and accurate machines. Manufacturing facilities that feature Industry 5.0 work settings require higher expectations, higher accuracy, sustainability solutions, mass customization of products, more human involvement, and digital technologies in smart workstations. Given these features, the cognitive load exerted on human workers in this environment is continuously increasing, leading to the use of cognitive heuristics. Cognitive biases are getting more attention in the cognitive ergonomics field, to help understand the operational behavior of workers. Manufacturing facilities can integrate cognitive assistance systems to work in parallel with physical and sensorial assistance systems. Cognitive assistance systems help toward better work conditions for workers and better overall system performance. This research explores the impact of human thinking style and using a cognitive assistance system on workers\u27 cognitive load, bias-related human performance, and user satisfaction. This research presents the design and experimental implementation of a research framework based on a well-established three-layer model for implementing Industry 5.0 in manufacturing. The research framework was designed to apply the dual-system theory and cognitive assistance in Assembly 5.0. Two experiments are presented to show the effectiveness of the proposed research framework. A cognitive assistance system was designed and compared to a benchmark system from LEGO ® Company. Subjective and objective measures were used to assess the thinking style, cognitive load, bias-related human performance, and user satisfaction in Assembly 5.0. As Industry 5.0 requires higher expectations, higher accuracy, smart workstations, and higher complexity, cognitive assistance systems can reduce the cognitive load and maintain the work efficiency and user satisfaction. Therefore, this work is important to industry to expand the use of cognitive ergonomic tools and employ them for A5.0 workers\u27 benefits

Emotional Biosensing: Exploring Critical Alternatives

Emotional biosensing is rising in daily life: Data and categories claim to know how people feel and suggest what they should do about it, while CSCW explores new biosensing possibilities. Prevalent approaches to emotional biosensing are too limited, focusing on the individual, optimization, and normative categorization. Conceptual shifts can help explore alternatives: toward materiality, from representation toward performativity, inter-action to intra-action, shifting biopolitics, and shifting affect/desire. We contribute (1) synthesizing wide-ranging conceptual lenses, providing analysis connecting them to emotional biosensing design, (2) analyzing selected design exemplars to apply these lenses to design research, and (3) offering our own recommendations for designers and design researchers. In particular we suggest humility in knowledge claims with emotional biosensing, prioritizing care and affirmation over self- improvement, and exploring alternative desires. We call for critically questioning and generatively re- imagining the role of data in configuring sensing, feeling, ‘the good life,’ and everyday experience

“WHAT’S WITH WHAT’S HER NAME? SIRI, CALL SO AND SO… CAN’T YOU USE YOUR OWN HANDS!?” OLDER ADULT PERSPECTIVES ON THE ROLES COMMUNICATION TECHNOLOGY AND PHYSICAL ACTIVITY PLAY IN THEIR AGING EXPERIENCES

Information and resources are needed to promote quality of life and access to health resources for older adults. Physical activity is an effective health prevention strategy that can help increase older adult life satisfaction and maintain functional ability. Communication technologies like the Internet and smartphone are also useful tools that provide older adults needed resources to stay educated and engaged in healthy aging. This study investigated older adult perceptions of aging, communication technology, and physical activity for older adults. Literature on theories of aging, physical activity strategies, behavior change, and technology use was gathered to understand how these concepts contribute to healthy aging. A mixed methods research design was used to understand older adult perceptions on aging, technology and physical activity. Constructivist Grounded Theory and Constant Comparative Analysis were used to qualitatively analyze interview and focus group transcripts for emergent themes on these topics. Hierarchical logistic and multiple regressions were used to quantitatively test the relationships between attitudes toward aging and technology beliefs, technology ownership/use and physical activity self-efficacy. Aging was found to be a time of a change that necessitated acceptance and adjustment to the declines associated with aging. Older adults in this study balanced their concern for the harms of the Internet and smartphones with the benefits these technologies offer. Participants were concerned about the ways communication technology may harm human interactions, but these older adults also admitted to the benefits of using these technologies to communicate with family and search for health information online. Also, physical activity was an important strategy participants used to maintain health and have control over their aging experiences. Attitudes toward aging were found to predict smartphone ownership in older adults, though this relationship was weak. The findings argue that society has something to learn from older adults’ balanced and mindful use of communication technology. This study also argues a sense of control in aging experiences may influence older adult physical activity self-efficacy. More research is needed to understand the complex role aging perceptions play in older adult physical activity and older adult use of technology for health purposes