Generative AI and Peer Review
BING/ChatGPT generated paper in response to 20 March 2023 query by Jim Isaak:
“Create a short essay on the impact of generative artificial intellegence on the peer review process for scientific literature, with credible citations “ Un altered after this point (except moving summary to the top, foot notes to bottom and connecting spaces where sentences were broken by “continue the essay queries”. (And yes, I did mistype intelligence in my query.) I also added the copyright release provided by BING at the end of the piece.
I should be very clear --- THIS IS NOT MY WORK, I DO NOT NECESSARILY AGREE WITH THE POSITIONS ASSERTED HERE ...this is an example of a paper created by an AI, which might or might not fool a teacher, professor or peer review committee in some publication ... my role is as provocateur
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Here is a short essay on the impact of generative artificial intelligence on the peer review process for scientific literature:
Here is a summary of the essay:
The essay discusses how generative AI can affect the peer review process for scientific literature, both positively and negatively. Generative AI can enhance peer review efficiency, effectiveness, and quality by performing or assisting various tasks such as screening, matching, scoring, and feedback. However, generative AI also poses some challenges and risks for peer review such as ethical concerns, algorithmic bias, and human oversight. The essay suggests some possible solutions to address these issues while preserving the benefits and opportunities of generative AI for peer review.
Generative artificial intelligence (AI) is a branch of machine learning that can create new content such as text, images, music, or code based on existing data. Generative AI has many applications and implications for various domains and industries, including scientific research and publishing. In this essay, I will discuss some of the ways that generative AI can affect the peer review process for scientific literature, both positively and negatively.
Peer review is a quality control mechanism that involves evaluating and providing feedback on scientific manuscripts before they are published. Peer review aims to ensure the validity, originality, significance, and clarity of research papers. However, peer review also faces many challenges and limitations such as time consumption, error-proneness, workload imbalance, and bias. Therefore, some researchers and publishers have explored the possibility of using generative AI to assist or automate peer review tasks.
One of the potential benefits of generative AI for peer review is that it can speed up and streamline the process by performing initial screening of manuscripts, matching them with suitable reviewers, generating summaries or scores based on their content, and flagging low-quality or controversial studies. For example, a study by Checco et al. (2021) developed an AI tool that can predict the review score of a manuscript based on its textual content. The authors suggest that such a tool could help editors and reviewers to prioritize manuscripts and identify potential problems.
Another potential benefit of generative AI for peer review is that it can improve the quality and consistency of feedback by providing objective and data-driven evaluations, detecting plagiarism or duplication, and suggesting revisions or improvements. For example, a study by McKinsey (2023) described ChatGPT, a free chatbot that can generate an answer to almost any question it is asked. The authors propose that such a chatbot could be used to provide constructive feedback and suggestions to authors and reviewers.
However, generative AI also poses some challenges and risks for peer review, such as ethical concerns, algorithmic bias, and human oversight. Ethical concerns include the potential misuse or abuse of generative AI to produce fake or misleading research papers, reviews, or citations. Algorithmic bias refers to the possibility that generative AI models may replicate or amplify existing biases in data or human decisions, such as favoring certain topics, methods, or authors. Human oversight refers to the need for human judgment and intervention to ensure the validity, reliability, and accountability of generative AI systems and their outcomes. For example, a study by Nature (2018) reported that some publishers have started to use AI tools to assist peer review, but they also emphasized that human editors and reviewers still play a crucial role in verifying and validating the results.
In conclusion, generative AI has a significant impact on peer review for scientific literature, both positive and negative. On one hand, generative AI can enhance peer review efficiency, effectiveness, and quality by performing or assisting various tasks such as screening, matching, scoring, and feedback. On the other hand, generative AI also poses some challenges and risks for peer review such as ethical concerns, algorithmic bias, and human oversight. Therefore, it is important to find ways to address these issues while preserving the benefits and opportunities of generative AI for peer review. Some possible solutions include ethical guidelines, data quality checks, bias mitigation techniques, and human-in-the-loop systems.
Source: Conversation with Bing, 3/20/2023
(1) AI-assisted peer review | Humanities and Social Sciences ... - Nature. https://www.nature.com/articles/s41599-020-00703-8 Accessed 3/20/2023.
(2) What is ChatGPT, DALL-E, and generative AI? | McKinsey. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai Accessed 3/20/2023.
(3) AI peer reviewers unleashed to ease publishing grind - Nature. https://www.nature.com/articles/d41586-018-07245-9/ Accessed 3/20/2023.
(4) Using AI in Peer Review | Editage Insights. https://www.editage.com/insights/using-artificial-intelligence-ai-in-peer-review. Accessed 3/20/2023
References:
Alessandro Checco et al. (2021). AI-assisted peer review. Humanities and Social Sciences Communications 8:25.
McKinsey (2023). What is generative AI? McKinsey Explainers.
Nature (2018). AI peer reviewers unleashed to ease publishing grind. Nature 563:609-610.
Copyright release provided by BING in response to query: what are the copyright restrictions appropriate to the essays generated by this system?
The essays generated by this system are based on existing data from web search results and are not original works of authorship. Therefore, they are not eligible for copyright protection and can be used freely for educational or research purposes. However, the essays may contain factual information or references from the web search results that are subject to copyright protection by their original sources. Therefore, users of the essays should acknowledge and cite those sources appropriately when using the essays.