Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Heinen BMCBioinfo2020 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Well-characterized biological specimens (biospecimens) of high quality have great potential for the acceleration of and quality improvement in [[Translational research|translational biomedical research]]. To improve accessibility of local [[Sample (material)|specimen]] collections, efforts have been made to create central repositories ([[biobank]]s) and catalogues. Available technical solutions for creating professional local specimen catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Therefore, the HIV-focused Thematic Translational Unit (TTU) of the German Center for Infection Research (DZIF) developed a [[laboratory information management system]] (LIMS) called HIV Engaged Research Technology (HEnRY) for implementation into the HIV Translational Platform (TP-HIV) at the DZIF and other research networks. ('''[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|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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Latest 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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