Scientific knowledge in the age of computation

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management : second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli, Efstathiou and Hislop in our analysis of the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge

Scientific knowledge in the age of computation: explicated, computable and manageable?

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management (KM):second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli (2014, 2016), Efstathiou (2012, 2016) and Hislop (2013) in our analysis of the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge.; Con el aumento de la publicación y la producción de datos, el conocimiento científico no solo es reconocido como un logro, sino también como un desafío. Las publicaciones y los datos científicos se tratan cada vez más como recursos que deben ser ‘gestionados’ digitalmente. Esto da lugar a la Gestión del Conocimiento científico (Knowledge Management (KM)): labor científica de segundo orden destinada a recopilar, cuidar y movilizar de forma directa el conocimiento disciplinario de primera mano y los datos para proporcionar nuevos conocimientos científicos de primer orden. Seguimos el trabajo de Leonelli (2014, 2016), Efstathiou (2012, 2016) y Hislop (2013) en nuestro análisis del uso de la KM en la biología de sistemas semánticos. A través de una descripción filosófica empírica de la investigación biológica habilitada para KM, argumentamos que KM ayuda a producir un nuevo conocimiento biológico de primer orden que no existía antes y que no podría haber sido producido por medios tradicionales. El trabajo de KM está facultado para concebir el “conocimiento” como un objeto para la ciencia computacional: como algo explicitado en el texto de artículos biológicos y como computable a través de datos y metadatos apropiados. Sin embargo, los conceptos fundados permiten el riesgo computacional de KM de centrarse solo en los datos que se pueden tratar de manera computacional como conocimiento, subestimando el conocimiento basado en la práctica y su importancia para garantizar la validez del conocimiento “manejable” como conocimiento

Scientific knowledge in the age of computation: Explicated, computable and manageable?

We have two theses about scientific knowledge in the age of computation. Our general claim is that scientific Knowledge Management practices emerge as second-order practices whose aim is to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data. Our specific thesis is that knowledge management practices are transforming biological research in at least three ways. We argue that scientific Knowledge Management a. operates with founded concepts of biological knowledge as explicated and computable, b. enables new outputs and ways of knowing within biology, and c. risks enforcing objectivist epistemologies of knowledge as some one objective thing

Emerging Economies and the Changing Dynamics of Development Cooperation

Almost two decades have passed since ‘emerging donors’ – new providers of development cooperation – began to attract the attention of ‘traditional donors’. Comprehensive comparisons of the various features of different types of donors have thus been elaborated on as their economic and political roles have solidified. Subsequently, the focus has also expanded to cover the growing significance of cooperation between ‘new donors’ themselves, beyond high-profile groups such as the BRICS (Brazil, Russia, India, China, and South Africa). This issue of the IDS Bulletin is a collaboration between the Japan International Cooperation Agency (JICA) Research Institute and the Institute of Development Studies (IDS). The studies are dedicated to highlighting the actual and potential contribution of a wider range of donors, and are intended to help compensate for imbalances in existing literature, contribute to scholarly understanding, and offer informative insights for practical policy deliberation. More fundamentally, this issue questions the explicit and implicit underlying assumptions in examining the issue of development cooperation: namely, the dichotomy of ‘traditional’ and ‘emerging’ donors. The subjects covered in the various articles range from exploring the importance of knowledge in the development cooperation experience of emerging economies; the challenges involved in the policy formulation and implementation of triangular cooperation within a sometimes contradictory set of processes embedded in South–South cooperation; the dynamics of ‘two-way interaction’ between donors and recipients in chains of knowledge creation; and there is an important reassessment of the critical data upon which estimates of China’s foreign aid disbursements are made and understood in international comparison.Japan International Cooperation Agency Research Institute (JICA

EMERGING ECONOMIES AND THE CHANGING DYNAMICS OF DEVELOPMENT COOPERATION

Almost two decades have passed since ‘emerging donors’ – new providers of development cooperation – began to attract the attention of ‘traditional donors’. Comprehensive comparisons of the various features of different types of donors have thus been elaborated on as their economic and political roles have solidified. Subsequently, the focus has also expanded to cover the growing significance of cooperation between ‘new donors’ themselves, beyond high-profile groups such as the BRICS (Brazil, Russia, India, China, and South Africa). This issue of the IDS Bulletin is a collaboration between the Japan International Cooperation Agency (JICA) Research Institute and the Institute of Development Studies (IDS). The studies are dedicated to highlighting the actual and potential contribution of a wider range of donors, and are intended to help compensate for imbalances in existing literature, contribute to scholarly understanding, and offer informative insights for practical policy deliberation. More fundamentally, this issue questions the explicit and implicit underlying assumptions in examining the issue of development cooperation: namely, the dichotomy of ‘traditional’ and ‘emerging’ donors. The subjects covered in the various articles range from exploring the importance of knowledge in the development cooperation experience of emerging economies; the challenges involved in the policy formulation and implementation of triangular cooperation within a sometimes contradictory set of processes embedded in South–South cooperation; the dynamics of ‘two-way interaction’ between donors and recipients in chains of knowledge creation; and there is an important reassessment of the critical data upon which estimates of China’s foreign aid disbursements are made and understood in international comparison

How to Make Agents and Influence Teammates: Understanding the Social Influence AI Teammates Have in Human-AI Teams

The introduction of computational systems in the last few decades has enabled humans to cross geographical, cultural, and even societal boundaries. Whether it was the invention of telephones or file sharing, new technologies have enabled humans to continuously work better together. Artificial Intelligence (AI) has one of the highest levels of potential as one of these technologies. Although AI has a multitude of functions within teaming, such as improving information sciences and analysis, one specific application of AI that has become a critical topic in recent years is the creation of AI systems that act as teammates alongside humans, in what is known as a human-AI team. However, as AI transitions into teammate roles they will garner new responsibilities and abilities, which ultimately gives them a greater influence over teams\u27 shared goals and resources, otherwise known as teaming influence. Moreover, that increase in teaming influence will provide AI teammates with a level of social influence. Unfortunately, while research has observed the impact of teaming influence by examining humans\u27 perception and performance, an explicit and literal understanding of the social influence that facilitates long-term teaming change has yet to be created. This dissertation uses three studies to create a holistic understanding of the underlying social influence that AI teammates possess. Study 1 identifies the fundamental existence of AI teammate social influence and how it pertains to teaming influence. Qualitative data demonstrates that social influence is naturally created as humans actively adapt around AI teammate teaming influence. Furthermore, mixed-methods results demonstrate that the alignment of AI teammate teaming influence with a human\u27s individual motives is the most critical factor in the acceptance of AI teammate teaming influence in existing teams. Study 2 further examines the acceptance of AI teammate teaming and social influence and how the design of AI teammates and humans\u27 individual differences can impact this acceptance. The findings of Study 2 show that humans have the greatest levels of acceptance of AI teammate teaming influence that is comparative to their own teaming influence on a single task, but the acceptance of AI teammate teaming influence across multiple tasks generally decreases as teaming influence increases. Additionally, coworker endorsements are shown to increase the acceptance of high levels of AI teammate teaming influence, and humans that perceive the capabilities of technology, in general, to be greater are potentially more likely to accept AI teammate teaming influence. Finally, Study 3 explores how the teaming and social influence possessed by AI teammates change when presented in a team that also contains teaming influence from multiple human teammates, which means social influence between humans also exists. Results demonstrate that AI teammate social influence can drive humans to prefer and observe their human teammates over their AI teammates, but humans\u27 behavioral adaptations are more centered around their AI teammates than their human teammates. These effects demonstrate that AI teammate social influence, when in the presence of human-human teaming and social influence, retains potency, but its effects are different when impacting either perception or behavior. The above three studies fill a currently under-served research gap in human-AI teaming, which is both the understanding of AI teammate social influence and humans\u27 acceptance of it. In addition, each study conducted within this dissertation synthesizes its findings and contributions into actionable design recommendations that will serve as foundational design principles to allow the initial acceptance of AI teammates within society. Therefore, not only will the research community benefit from the results discussed throughout this dissertation, but so too will the developers, designers, and human teammates of human-AI teams