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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Naphade JofClinDiagRes2023 17-2.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Quality control in the clinical biochemistry laboratory: A glance|Quality control in the clinical biochemistry laboratory: A glance]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


[Quality control]] (QC) is a process, designed to ensure reliable test results. It is part of overall [[laboratory]] quality management in terms of accuracy, reliability, and timeliness of reported test results. Two types of QC are exercised in [[Clinical chemistry|clinical biochemistry]]: internal QC (IQC) and external [[quality assurance]] (QA). IQC represents the quality methods performed every day by laboratory personnel with the laboratory’s materials and equipment. It primarily checks the precision (i.e., repeatability or reproducibility) of the test method. External quality assurance service (EQAS)  is performed periodically (i.e., every month, every two months, twice a year) by the laboratory personnel, who primarily are checking the accuracy of the laboratory’s analytical methods ... ('''[[Journal:Quality control in the clinical biochemistry laboratory: A glance|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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