Related papers: Data Science as a New Frontier for Design
Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the…
Modern data and applications pose very different challenges from those of the 1950s or even the 1980s. Students contemplating a career in statistics or data science need to have the tools to tackle problems involving massive, heavy-tailed…
Technology adoption research aims to determine the reasons why and how individuals, corporations, and industries start using new technology. Furthermore, technology adoption itself is decomposed into underlying sub-processes which are…
The phenomenon of innovation has been shifting away from focusing on tangible to intangible modernization with its vitalizing context. This shift appears vitally in innovation developed by individual end-users in organizations and…
Evaluation of potential AGI systems and methods is difficult due to the breadth of the engineering goal. We have no methods for perfect evaluation of the end state, and instead measure performance on small tests designed to provide…
Ethics in the emerging world of data science are often discussed through cautionary tales about the dire consequences of missteps taken by high profile companies or organizations. We take a different approach by foregrounding the ways that…
The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of Machine Learning…
The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine…
Many Artificial Intelligence systems depend on the agent's updating its beliefs about the world on the basis of experience. Experiments constitute one type of experience, so scientific methodology offers a natural environment for examining…
Algorithms and technologies are essential tools that pervade all aspects of our daily lives. In the last decades, health care research benefited from new computer-based recruiting methods, the use of federated architectures for data…
Contemporary software systems, such as the Internet of Things, Industry 4.0 and Intelligent Cities, present challenges for their engineering, since they question our traditional form of software development. They represent a promising…
This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model. The proposed model moves away from the conventional node-centric framework and focuses on…
The authors design and demonstrate a process for carrying out design science (DS) research in information systems and demonstrate use of the process to conduct research in two case studies. Several IS researchers have pioneered the…
We present a methodology for evidence based design of cryptoeconomic systems, and elucidate a real-world example of how this methodology was used in the design of a blockchain network. This work provides a rare insight into the application…
Open Science aims to foster openness and collaboration in research, leading to more significant scientific and social impact. However, practicing Open Science comes with several challenges and is currently not properly rewarded. In this…
Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of…
This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. In Data Feminism (2020), we offered seven principles for examining and challenging unequal power in data…
Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health,…
The availability of large amounts of data together with advances in analytical techniques afford an opportunity to address difficult challenges in ensuring that healthcare is safe, effective, efficient, patient-centered, equitable, and…
Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented…