Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Sustainability Standards and Stakeholder Engagement: Lessons From Carbon Markets

Stakeholders play an increasingly active role in private governance, including development of standards for measuring sustainability. Building on prior studies focused on standards and stakeholder engagement, we use an innovation management theoretical lens to compare stakeholder engagement and standards developed in two carbon markets: the Climate Action Reserve and the U.N.’s Clean Development Mechanism. We develop and test hypotheses regarding how different processes of stakeholder engagement in standard development affect the number, identity, and age of stakeholders involved, as well as the variation and quality of the resulting standards. In doing so, we contribute to the growing literature on stakeholder engagement in developing sustainability standards

End to End Learning in Autonomous Driving Systems

Convolutional neural networks have advanced visual perception significantly in recent years. Two major ingredients that enable such a success are the composition of simple modules into a complex network and the end to end optimization. However, such success has not yet revolutionized robotics as much as vision, even if robotics suffer from similar problems as traditional computer vision, i.e. imperfectness of the manual pipeline design of the system. This thesis investigates using end-to-end learning for the autonomous driving system, a concrete robotic application. End to end learning can produce reasonable driving behaviors, even in the complex urban driving scenarios. Representation learning in end-to-end driving models is crucial, and auxiliary vision tasks such as semantic segmentation can help to form a more informative driving representation especially when training data is limited. Naive convolutional neural networks are usually only capable of doing reactive control and can not involve complex reasoning in a particular scenario. This thesis also studies how to handle scene conditioned driving behavior, which goes beyond the capability of reactive control. Alongside the end-to-end structure, learning methods also play a critical role. Imitation learning methods will acquire meaningful behaviors but usually, the robot can not master the skill. Reinforcement learning, on the contrary, either barely learns anything if the environment is too complex, or it can master the skill otherwise. To get the best of both worlds, this thesis proposes an algorithmically unified method to learn from both demonstration data and the environment

Partnering People with Deep Learning Systems: Human Cognitive Effects of Explanations

Advances in “deep learning” algorithms have led to intelligent systems that provide automated classifications of unstructured data. Until recently these systems could not provide the reasons behind a classification. This lack of “explainability” has led to resistance in applying these systems in some contexts. An intensive research and development effort to make such systems more transparent and interpretable has proposed and developed multiple types of explanation to address this challenge. Relatively little research has been conducted into how humans process these explanations. Theories and measures from areas of research in social cognition were selected to evaluate attribution of mental processes from intentional systems theory, measures of working memory demands from cognitive load theory, and self-efficacy from social cognition theory. Crowdsourced natural disaster damage assessment of aerial images was employed using a written assessment guideline as the task. The “Wizard of Oz” method was used to generate the damage assessment output of a simulated agent. The output and explanations contained errors consistent with transferring a deep learning system to a new disaster event. A between-subjects experiment was conducted where three types of natural language explanations were manipulated between conditions. Counterfactual explanations increased intrinsic cognitive load and made participants more aware of the challenges of the task. Explanations that described boundary conditions and failure modes (“hedging explanations”) decreased agreement with erroneous agent ratings without a detectable effect on cognitive load. However, these effects were not large enough to counteract decreases in self-efficacy and increases in erroneous agreement as a result of providing a causal explanation. The extraneous cognitive load generated by explanations had the strongest influence on self-efficacy in the task. Presenting all of the explanation types at the same time maximized cognitive load and agreement with erroneous simulated output. Perceived interdependence with the simulated agent was also associated with increases in self-efficacy; however, trust in the agent was not associated with differences in self-efficacy. These findings identify effects related to research areas which have developed methods to design tasks that may increase the effectiveness of explanations

Using crowdsourced geospatial data to aid in nuclear proliferation monitoring

In 2014, a Defense Science Board Task Force was convened in order to assess and explore new technologies that would aid in nuclear proliferation monitoring. One of their recommendations was for the director of National Intelligence to explore ways that crowdsourced geospatial imagery technologies could aid existing governmental efforts. Our research builds directly on this recommendation and provides feedback on some of the most successful examples of crowdsourced geospatial data (CGD). As of 2016, Special Operations Command (SOCOM) has assumed the new role of becoming the primary U.S. agency responsible for counter-proliferation. Historically, this institution has always been reliant upon other organizations for the execution of its myriad of mission sets. SOCOM's unique ability to build relationships makes it particularly suited to the task of harnessing CGD technologies and employing them in the capacity that our research recommends. Furthermore, CGD is a low cost, high impact tool that is already being employed by commercial companies and non-profit groups around the world. By employing CGD, a wider whole-of-government effort can be created that provides a long term, cohesive engagement plan for facilitating a multi-faceted nuclear proliferation monitoring process.http://archive.org/details/usingcrowdsource1094551570Major, United States ArmyMajor, United States ArmyApproved for public release; distribution is unlimited

Knowledge orchestration and digital innovation networks: insights from the Chinese context

As digital innovation increasingly pushes heterogeneous actors to connect with each other across multiple organizational and community boundaries, a doubly distributed innovation network may emerge, leading to the knowledge being too fragmented and heterogeneous. Facing this problem, I place an emphasis on material artefacts and social network structures in the cultural context of Chinese digital innovators. On the one hand, as innovation is increasingly mediated by material artefacts, I focus on epistemic objects and activity objects, which are able to motivate the process of innovation. On the other hand, as innovation transforms the network actors’ social space, I focus on the role of “guanxi” (i.e. a system of influential relationships in Chinese culture) and structural holes (i.e. the absence of a connection between two contacts) in digital innovation networks. At the same time, as the literature recognizes knowledge orchestration as a useful starting point to address the knowledge fragmentation and heterogeneity, I identify five activities as knowledge orchestration: knowledge mobilization, knowledge coordination, knowledge sharing, knowledge acquisition and knowledge integration. As traditional tools used to support knowledge management can no longer handle the fragmented and heterogeneous knowledge, there is limited studies contributing to our understanding of how the Chinese innovators use objects and social network structures to orchestrate knowledge in their innovation networks. With these paucities of research in mind, this thesis explores how the material objects and the social network structures orchestrate knowledge for coordinating the fragmented and heterogeneous knowledge in Chinese digital innovation networks. From the perspective of material artefacts, my first study explores how epistemic objects affect the acquisition, integration and sharing of knowledge among collaborative organizations during their IT innovation alliances. My second study explores how activity objects affect the sharing, acquisition and integration of knowledge for crowdsourced digital innovation. From a social perspective, my third study explores how guanxi and structural holes affect the mobilization and coordination of knowledge among Chinese digital entrepreneurs in their innovation networks. Following the three studies, I show my key contributions, and discuss my theoretical and practical implications

Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar - all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.Comment: 40 pages main paper, 5 pages appendi

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends