Resources
FAIR 101
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The latest developments on FAIR are available at GO-FAIR.
Find or deposit models
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DLHub // https://www.dlhub.org/
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OpenML // https://www.openml.org/
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AI Model Share // https://www.modelshare.org/
Machine Learning
Interesting Reads
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FAIR AI Models in High Energy Physics: From our colleagues at UC San Diego, NCSA, and others, this article studies the robustness of FAIR AI models and their portability across hardware architectures and software frameworks, and reports new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. It could offer an approach for other scientific domains on how to make their AI models FAIR.
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AI-Ready Open Data: This Bipartisan Policy Center article discusses what it means for data to be "AI-ready" and how to implement this data in a strategical and ethical way. The article creates a clear path for policymakers to move the agenda forward in three actionable ways.
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The Reproducibility Issues that Haunt Healthcare AI: This Nature article asks the question: Health-care systems are rolling out artificial-intelligence tools for diagnosis and monitoring. But how reliable are the models?
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The Reproducibility Crisis in ML-based Science: Hosted by the Center for Statistics and Machine Learning at Princeton University, this piece highlights the scale and scope of the crisis, identifies root causes of the observed reproducibility failures, and suggests progress towards solutions.
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Strengthening and Democratizing the U.S. Artificial Intelligence Innovation Ecosystem is a document that describes "An Implementation Plan for A National Artificial Intelligence Research Resource."
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ORCID & GO FAIR US: Collaborators Working to Realize a FAIR Data Ecosystem - recent blog post by Nancy Hoebelheinrich and Shawna Sadler titled, “ORCID & GO FAIR US: Collaborators Working to Realize a FAIR Data Ecosystem.” This blog takes a deep dive into the FAIR Data Ecosystem and highlights how partnership between the two organizations can provide collaborative support to researchers.
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Are the FAIR Data Principles fair? - This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles.
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Are Institutional Research Data Policies in the US Supporting the FAIR Principles? A Content Analysis - We discuss ways in which these institutional policies represent a missed opportunity to implement the FAIR principles and suggest ways policies could be modified to encourage researchers to follow them. We also discuss future research opportunities to examine how policy implementation may affect what institutional support researchers receive.
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Perspective: Unlocking ML requires an ecosystem approach This article calls out the differences between traditional software development and the ML lifecycle where "data is the new code".
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International Supercomputing Conference Session Explores Ethics in AI and HPC