Related papers: FAIR-CS: Framework for Interdisciplinary Research …
Interdisciplinary collaboration is essential for the advance of research. As domain subjects become more and more specialized, researchers need to cross disciplines for insights from peers in other areas to have a broader and deeper…
As generative AI becomes embedded in higher education, it increasingly shapes how students complete academic tasks. While these systems offer efficiency and support, concerns persist regarding over-automation, diminished student agency, and…
Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific…
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI)…
In a world of daily emerging scientific inquisition and discovery, the prolific launch of machine learning across industries comes to little surprise for those familiar with the potential of ML. Neither so should the congruent expansion of…
Over the past few decades, ubiquitous sensors and systems have been an integral part of humans' everyday life. They augment human capabilities and provide personalized experiences across diverse contexts such as healthcare, education, and…
In recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of data-intensive scientific disciplines. These digital objects…
The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. However, these principles have not yet been broadly adopted in…
Reproducibility and replicability of research findings are central to the scientific integrity of epidemiology. In addition, many research questions require combiningdata from multiple sources to achieve adequate statistical power. However,…
This research studies the impact of AI and peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform - a product of research into collaborative learning, big data, and artificial intelligence…
Justice-centered approaches to equitable computer science (CS) education frame CS learning as a means for advancing peace, antiracism, and social justice rather than war, empire, and corporations. However, most research in justice-centered…
There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles…
Equity is crucial to the ethical implications in technology development. However, implementing equity in practice comes with complexities and nuances. In response, the research community, especially the human-computer interaction (HCI) and…
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in…
A challenge is to develop cyber-physical system scenarios that reflect the diversity and complexity of real-life cyber-physical systems in the research questions that they address. Time-bounded collaborative events, such as hackathons, jams…
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or the sheer volume of data generated. Federated learning (FL)…
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is important for ethical compliance. However, there exist challenges…
As algorithms increasingly take on critical roles in high-stakes areas such as credit scoring, housing, and employment, civil enforcement actions have emerged as a powerful tool for countering potential discrimination. These legal actions…
The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets…
Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science…