Online creative collaboration is challenging our basic assumptions about how people can create together. Volunteers from around the world who meet and communicate over the Internet have written the world's largest encyclopedia, developed market-leading software products, solved important open problems in mathematics, and produced award-winning films, among many examples. A growing body of research refutes the popular myth that these projects succeed through "self-organization" and instead points to the critical importance of effective leadership. Yet, we know little about what these leaders actually do, the challenges they must manage, and how technology supports or hinders their efforts. In this dissertation, I investigated the role of leadership in online creative collaboration. I first conducted two empirical studies of existing leadership practices, focusing on the domain of online, collaborative animation projects called "collabs." In the first study, I identified the major challenges faced by collab leaders. In the second study, I identified leader traits and behaviors correlated with success. These initial findings suggested that many collab leaders, overburdened and lacking adequate technological support, respond by attempting less ambitious projects and adopting centralized leadership styles. Despite these efforts, leaders frequently become overburdened, and more than 80% of collabs fail. To ease the burden on leaders and encourage more complex, successful projects, I led the development of a web-based, open-source software tool called Pipeline. Pipeline can support leadership by reinforcing a traditional, top-down approach, or transform leadership by redistributing it across many members of a group. This latter approach relies on social processes, rather than technical constraints, to guide behavior. I evaluated Pipeline's ability to effectively support and transform leadership through a detailed case study of Holiday Flood, a six-week collaboration involving nearly 30 artists from around the world. The case study showed that formal leaders remained influential and Pipeline supported their traditional, centralized approach. However, there was also evidence that Pipeline transformed some leadership behaviors, such as clarifying, informing, and monitoring, by redistributing them beyond the project's formal leaders. The result was a significantly more ambitious project which attained its goals and earned high praise from the community. The main contributions of this dissertation include: (1) a rich description of existing leadership practices in online creative collaboration; (2) the development of redistributed leadership as a theoretical framework for analyzing the relationship between leadership and technological support; (3) design implications for supporting and transform leadership; (4) a case study illustrating how technology can support and transform leadership in the real world; and (5) the Pipeline collaboration tool itself, released as open-source software.

This research has examined how Information and Communication Technologies (ICTs) can promote healthy eating habits amongst African Americans in low-income neighborhoods, a population that faces disproportionately high rates of diet-related health problems. In this dissertation, I describe the formative research I conducted to obtain system design guidelines and how I used those guidelines to develop two applications: EatWell and Community Mosaic. I also describe the results of the in-depth field studies I conducted to evaluate each application. Both EatWell and Community Mosaic incorporate the cultural construct of collectivism, a social orientation in which interdependence and communal responsibility are valued over individual goals and independence. As researchers have generally characterized the African American culture as collectivistic and argued for the value of designing collectivistic health interventions for this population, I examined the implications of taking such an approach to designing health promotion technologies. EatWell and Community Mosaic are collectivistic because they empower users to care for the health of their local community by helping others learn practical, locally-relevant healthy eating strategies. I discuss the results of my formative fieldwork and system evaluations, which characterize the value, challenge and nuances of developing community-based health information sharing systems for specific cultural contexts. By focusing on health disparities issues and the community social unit, I extend previous health technology research within Human-Computer Interaction (HCI). In particular, my results describe 1) a set of characteristics that help make shared material useful and engaging, 2) how accessing this information affects how people view the feasibility of eating well in their local context, 3) the way in which sharing information actually benefits the contributor by catalyzing personal behavior reflection, analysis and modification and 4) how sharing information and seeing that information's impact on others can help to build individuals' capacity to be a community health advocate. In addition, my work shows how examining cultural generalizations such as collectivism is not a straightforward process but one that requires careful investigation and appreciation for the way in which such generalizations are (or are not) manifested in the lives of individual people. I further contribute to HCI by presenting a set of important considerations that researchers should make when designing and evaluating community-based health systems. I conclude this dissertation by outlining directions for future HCI research that incorporates an understanding of the relationship between culture and health and that attempts to address health disparities in the developed world.

This research examines new systems of information production that are made possible by participatory media. Such systems bring about two critical information literacy needs for the general public: to understand new systems in order to assess their products and to become adept participants in the construction of public information spaces. In this dissertation, I address both of these needs and propose a view of information literacy that situates the information literate as both consumer and producer. First, I examine a popular example of a new publishing system, Wikipedia, and present research that explains how the site is organized and maintained. I then turn my attention to the classroom and describe three iterations of design-based research in which I built new wiki tools to support publication activities and information literacy learning in formal educational contexts. I use the rhetorical notion of genre as an analytic lens for studying the use and impact of these new media in schools. Classroom findings suggest that the affordances of a wiki as an open, transparent publishing medium can support groups of writers in building a shared understanding of genre as they struggle with an unfamiliar rhetorical situation. I also demonstrate how writing on a public wiki for a broad audience was a particularly useful writing experience that brought about opportunities for reflection and learning. These opportunities include transforming the value of citation, creating a need to engage deeply with content, and providing both a need and a foundation for assessing information resources.

<p>The availability of abundant multimodal data, including textual, visual, and sensor-based information, holds the potential to improve decision-making in diverse domains. Extracting data-driven decision-making information from heterogeneous and changing datasets in real-world data-centric applications requires achieving complementary functionalities of multimodal data integration, knowledge extraction and mining, situationally-aware data recommendation to different users, and uncertainty management in the open-world setting. To achieve a system that encompasses all of these functionalities, several challenges need to be effectively addressed: (1) How to represent and analyze heterogeneous source contents and application context for multimodal data recommendation? (2) How to predict and fulfill current and future needs as new information streams in without user intervention? (3) How to integrate disconnected data sources and learn relevant information to specific mission needs? (4) How to scale from processing petabytes of data to exabytes? (5) How to deal with uncertainties in open-world that stem from changes in data sources and user requirements?</p> <p><br></p> <p>This dissertation tackles these challenges by proposing novel frameworks, learning-based data integration and retrieval models, and algorithms to empower decision-makers to extract valuable insights from diverse multimodal data sources. The contributions of this dissertation can be summarized as follows: (1) We developed SKOD, a novel multimodal knowledge querying framework that overcomes the data representation, scalability, and data completeness issues while utilizing streaming brokers and RDBMS capabilities with entity-centric semantic features as an effective representation of content and context. Additionally, as part of the framework, a novel text attribute recognition model called HART was developed, which leveraged language models and syntactic properties of large unstructured texts. (2) In the SKOD framework, we incrementally proposed three different approaches for data integration of the disconnected sources from their semantic features to build a common knowledge base with the user information need: (i) EARS: A mediator approach using schema mapping of the semantic features and SQL joins was proposed to address scalability challenges in data integration; (ii) FemmIR: A data integration approach for more susceptible and flexible applications, that utilizes neural network-based graph matching techniques to learn coordinated graph representations of the data. It introduces a novel graph creation approach from the features and a novel similarity metric among data sources; (iii) WeSJem: This approach allows zero-shot similarity matching and data discovery by using contrastive learning<br> to embed data samples and query examples in a high-dimensional space using features as a novel source of supervision instead of relevance labels. (3) Finally, to manage uncertainties in multimodal data management for open-world environments, we characterized novelties in multimodal information retrieval based on data drift. Moreover, we proposed a novelty detection and adaptation technique as an augmentation to WeSJem.<br> </p> <p>The effectiveness of the proposed frameworks, models, and algorithms was demonstrated<br> through real-world system prototypes that solved open problems requiring large-scale human<br> endeavors and computational resources. Specifically, these prototypes assisted law enforcement officers in automating investigations and finding missing persons.<br> </p>

<p dir="ltr">In the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory.</p><p dir="ltr">A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security.</p><p dir="ltr">The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with exploring quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms.</p><p dir="ltr">In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.</p>

<p dir="ltr">Humans are social beings and throughout our evolution we have survived and thrived thanks to our ability to cooperate [7]. Overcoming our current societal challenges from sustainability and energy conservation [8] to democracy, public health, and community building [9] will all require our continued cooperation. Yet, many of these present us with a dilemma where our short-term personal goals are at odds with the collective long-term benefits. For example, many of us listen NPR radio but never make a donation to help cover its operational costs. The success of cooperation during such dilemmatic situations often depends on communication, reward and punishment structures, social norms and cues [10], [11], [12], [13]. But how to encourage cooperation online where social cues are not readily available?</p><p dir="ltr">Accelerated by the COVID-19 pandemic and the prevalence of digital technologies, cooperation among individuals increasingly happens online where data-based feedback supports our decisions. Problematically, people online are often not only remote and asynchronous, but often also anonymous, which has resulted in de-individuation and antinormative behavior [14]. Social data, information that users share about themselves via digital technologies, may offer opportunities for social feedback design that affords perceptions of social cohesion and may support successful cooperation online.</p><p dir="ltr">This dissertation seeks to answer the normative question of how to design for cooperation in social data feedback charts in dilemmatic situations online. I conducted mixed methods design research by combining theory-driven design with a series of controlled experiments on Amazon Mechanical Turk to understand the perceptual and behavioral effects of visually unifying social data feedback charts. To achieve this, I mapped the design space for home energy feedback (<i>Chapter 2</i>) to guide my iterative and user-centered theorizing about how visual unity in social feedback charts might prime viewers with unified group perceptions (<i>Chapter 3</i>). I then validated my theorizing with controlled perceptual (<i>Chapter 4</i>) and decision experiments (<i>Chapter 5</i>).</p><p dir="ltr">The triangulated results offer evidence for visually unifying cues in feedback charts affecting social data interpretation (<i>Chapter 4</i>) and cooperation online (<i>Chapter 5</i>). Two visual properties: data point <i>proximity</i> and <i>enclosure</i> -, trigger variable levels of perceivable social unity that play a partial role in participants’ decision to cooperate in a non-monetary social dilemma situation online. I discuss the implications for future research and design (<i>Chapter 6</i>).</p>