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GRAIL: Developing responsible practices for AI and machine learning in research funding and evaluation with a community of learning

Authors: Newman-Griffis, Denis;

GRAIL: Developing responsible practices for AI and machine learning in research funding and evaluation with a community of learning

Abstract

Discussion paper for 2024 Data for Policy Conference. New developments in artificial intelligence (AI) and machine learning (ML) technologies are opening new avenues for research funding organisations to learn from the rich data sources and internal expertise they have curated over decades, and to develop new data-driven practices in response to rapid scientific development and changing policy environments. However, there is a lack of shared experience and best practice in using AI and ML in the work of research funding and evaluation, and it is often unclear how developments in AI Safety and Responsible AI discourses translate into practical insights for complex organisations like research funders. The Research on Research Institute’s GRAIL project is an ongoing effort drawing on a community of learning among research funding organisations to develop specific insights, pathways, and critical questions to guide responsible use of AI and ML in the research funding ecosystem. This extended abstract highlights emerging themes and learning opportunities from the ongoing GRAIL workshop series, as key directions of travel for developing best practice around the use of AI and ML in the research funding ecosystem. The GRAIL project is funded by the Research on Research Institute.

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Keywords

research evaluation, Artificial intelligence, Machine learning, AI evaluation, responsible AI, research funding

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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