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'''"[[Journal:Towards a risk catalog for data management plans|Towards a risk catalog for data management plans]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Although [[Information management|data management]] and its careful planning are not new topics, there is little published research on [[Risk management|risk mitigation]] in data management plans (DMPs). We consider it a problem that DMPs do not include a structured approach for the [[Risk assessment|identification]] or mitigation of risks, because it would instill confidence and trust in the data and its stewards, and foster the successful conduction of data-generating projects, which often are funded research projects. In this paper, we present a lightweight approach for identifying general risk in DMPs. We introduce an initial version of a generic risk catalog for funded research and similar projects. By analyzing a selection of 13 DMPs for projects from multiple disciplines published in the ''Research Ideas and Outcomes'' (''RIO'') journal, we demonstrate that our approach is applicable to DMPs and transferable to multiple institutional constellations. As a result, the effort for integrating risk management in data management planning can be reduced. ('''[[Journal:Towards a risk catalog for data management plans|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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Revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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