This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community.
more »
« less
VREs/Virtual Labs/Science Gateways: How could FAIRness badges for providers, developers and users of VREs look like?
VREs are predestined to support many aspects of FAIR because of their characteristics to provide a workspace for collaboration, sharing data and simulations and/or workflows. The FAIR for VRE Working Group has worked on a checklist to measure FAIRness in science gateways. This list considers how to address the complexity in regard to which target group is addressed – developers or users – and the granularity such as VREs as software frameworks, services, APIs, workflows, data and simulations. We assume that not only VREs as software frameworks are FAIR but that they also are FAIR-enabling for the digital objects they contain. The objective of this session will be how to recognize and incentivize that providers, developers and users are actively working towards FAIRness of digital objects. The idea for this session is to address this via badges. It probably makes sense to split the badges for the four principles Findable, Accessible, Interoperable and Reusable. There are many open questions beyond this granularity such as how to create badges, who gives such badges, what are the rules for the duration of badges?
more »
« less
- Award ID(s):
- 2231406
- PAR ID:
- 10533738
- Publisher / Repository:
- Zenodo
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real‐world scenario— where diverse opinions are represented by a larger collection of rankings— remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks. We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision‐making in ranking based decision‐making contexts. Code, datasets and demo video available at:osf.io/d7cwkmore » « less
-
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in personalized recommendation. We formulate our fair personalized recommendation as a modified contextual bandit and focus on achieving fairness on the individual whom is being recommended an item as opposed to achieving fairness on the items that are being recommended. We introduce and define a metric that captures the fairness in terms of rewards received for both the privileged and protected groups. We develop a fair contextual bandit algorithm, Fair-LinUCB, that improves upon the traditional LinUCB algorithm to achieve group-level fairness of users. Our algorithm detects and monitors unfairness while it learns to recommend personalized videos to students to achieve high efficiency. We provide a theoretical regret analysis and show that our algorithm has a slightly higher regret bound than LinUCB. We conduct numerous experimental evaluations to compare the performances of our fair contextual bandit to that of LinUCB and show that our approach achieves group-level fairness while maintaining a high utility.more » « less
-
null (Ed.)Algorithmic bias and fairness in the context of graph mining have largely remained nascent. The sparse literature on fair graph mining has almost exclusively focused on group-based fairness notation. However, the notion of individual fairness, which promises the fairness notion at a much finer granularity, has not been well studied. This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM). First, we present a generic definition of individual fairness for graph mining which naturally leads to a quantitative measure of the potential bias in graph mining results. Second, we propose three mutually complementary algorithmic frameworks to mitigate the proposed individual bias measure, namely debiasing the input graph, debiasing the mining model and debiasing the mining results. Each algorithmic framework is formulated from the optimization perspective, using effective and efficient solvers, which are applicable to multiple graph mining tasks. Third, accommodating individual fairness is likely to change the original graph mining results without the fairness consideration. We conduct a thorough analysis to develop an upper bound to characterize the cost (i.e., the difference between the graph mining results with and without the fairness consideration). We perform extensive experimental evaluations on real-world datasets to demonstrate the efficacy and generality of the proposed methods.more » « less
-
null (Ed.)As digital fabrication machines become widespread, online communities have provided space for diverse practitioners to share their work, troubleshoot, and socialize. These communities pioneer increasingly novel fabrication workflows, and it is critical that we understand and conceptualize these workflows beyond traditional manufacturing models. To this end, we conduct a qualitative study of #PlotterTwitter, an online community developing custom hardware and software tools to create artwork with computer-controlled drawing machines known as plotters. We documented and analyzed emergent themes where the traditional interpretation of digital fabrication workflows fails to capture important nuances and nascent directions. We find that #PlotterTwitter makers champion creative exploration of interwoven digital and physical materials over a predictable series of steps. We discuss how this challenges long-running views of digital fabrication and propose design implications for future frameworks and toolkits to account for this breadth of practice.more » « less
An official website of the United States government

