AI research assistants, intrinsic values, and the science we want

Áreas de investigación:

• Sin categoría

Año:

2024

Tipo de publicación:

Artículo

Autores:

• Guersenzvaig, Ariel
• Sánchez-Monedero, Javier

Journal:

AI & SOCIETY

Mes:

Febrero

ISSN:

1435-5655

BibTex:

@article{research_assistants_2024, author = "Ariel Guersenzvaig and Javier S{\'a}nchez-Monedero", abstract = "Since their mass introduction in late 2022, AI chatbots like ChatGPT have garnered considerable attention due to the promise of widespread applications. Their purported advanced writing capacity has made it difficult for experts to differentiate between machine-generated and human-generated paper abstracts, as reported in Nature (Else 2023). However, many scholars emphasize that these systems should be seen as ‘stochastic parrots’ due to their lack of true understanding (Bender et al. 2021). Furthermore, these systems have been prone to produce ‘hallucinations’ (i.e., falsehoods), among other highlighted issues. This is not the venue for an exhaustive critique; our purpose is to comment on a rather specific topic: the use of chatbots for the automation of research and bibliographical review that tends to precede all academic research. As an example, consider Elicit, a tool that aims to optimize the flow of academic research. According to its developing company, ‘If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table{\&}#039; (https:elicit.org, faq. xxxx). It apparently does this by finding the most important information from the eight most {\&}#039;relevant{\&}#039; articles among a selection of 400 articles that are related to the question. Alternatively, think of Perplexity Copilot (https:blog.perplexity.ai, faq, what-is-copilot. xxxx), which offers a ‘tailored list of sources and even summarized papers’ to students and academics. We often teach our students that through bibliographical research, we find out what has been said about a topic, what other related views or theories exist, what gaps are still to be filled, and so on. Importantly, we emphasize that it serves to establish the foundations of our own research. But is the review just a mere instrument that we could optimize using tools like Elicit and Perplexity Copilot? To answer this question, we must first take a detour to address a more general issue related to the way science can be carried out.", awards = "JCR 2022: 3.0, Position: 126/192 (Q3), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR 2022: 3.0, Position: 126/192 (Q3), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s00146-023-01861-4", issn = "1435-5655", journal = "AI {\&}amp; SOCIETY", month = "Febrero", note = "JCR 2022: 3.0, Position: 126/192 (Q3), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", title = "{AI} research assistants, intrinsic values, and the science we want", url = "link.springer.com/article/10.1007/s00146-023-01861-4", year = "2024", }

Nota:

JCR 2022: 3.0, Position: 126/192 (Q3), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

Since their mass introduction in late 2022, AI chatbots like ChatGPT have garnered considerable attention due to the promise of widespread applications. Their purported advanced writing capacity has made it difficult for experts to differentiate between machine-generated and human-generated paper abstracts, as reported in Nature (Else 2023). However, many scholars emphasize that these systems should be seen as ‘stochastic parrots’ due to their lack of true understanding (Bender et al. 2021). Furthermore, these systems have been prone to produce ‘hallucinations’ (i.e., falsehoods), among other highlighted issues. This is not the venue for an exhaustive critique; our purpose is to comment on a rather specific topic: the use of chatbots for the automation of research and bibliographical review that tends to precede all academic research. As an example, consider Elicit, a tool that aims to optimize the flow of academic research. According to its developing company, ‘If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table' (https:elicit.org, faq. xxxx). It apparently does this by finding the most important information from the eight most 'relevant' articles among a selection of 400 articles that are related to the question. Alternatively, think of Perplexity Copilot (https:blog.perplexity.ai, faq, what-is-copilot. xxxx), which offers a ‘tailored list of sources and even summarized papers’ to students and academics. We often teach our students that through bibliographical research, we find out what has been said about a topic, what other related views or theories exist, what gaps are still to be filled, and so on. Importantly, we emphasize that it serves to establish the foundations of our own research. But is the review just a mere instrument that we could optimize using tools like Elicit and Perplexity Copilot? To answer this question, we must first take a detour to address a more general issue related to the way science can be carried out.

Comentarios:

JCR 2022: 3.0, Position: 126/192 (Q3), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE