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'''[[Evolutionary informatics]]''' is a sub-branch of [[informatics]] that addresses the algorithmic and technological tools (like information and analytical systems) needed to better manage data from research in ecology and evolutionary biology and answer evolutionary questions. As in [[bioinformatics]] and [[genomics]], scientists studying biological evolution have gathered an increasingly large volume of information, resulting in information management problems. Additionally, as bioinformatics and genomics are pertinent to the study of evolution, utilization of information from those areas is of concern in evolutionary informatics.
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Evolutionary informatics has evolved out of a wide variety of scientific, mathematical, and computational endeavors, including evolutionary biology, evolutionary computation, algorithmic and evolutionary algorithmic research, and software development. It can help tackle problems and tasks such as reducing "the growing number of lineages that lack formal taxonomic names," digitizing and semantically enhancing legacy biodiversity data while also making it more portable, and building "sustainable digital community repositories that provide access to rich data and metadata" in the field of evolutionary biology. ('''[[Evolutionary informatics|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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''Recently featured'':
''Recently featured'': [[Scientific data management system]], [[Centers for Disease Control and Prevention]], [[Histopathology]]
{{flowlist |
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
}}

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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