{"title":"AI 耻辱:学术作家和研究人员中无声的耻辱。","authors":"Louie Giray","doi":"10.1007/s10439-024-03582-1","DOIUrl":null,"url":null,"abstract":"<div><p>AI shaming refers to the practice of criticizing or looking down on individuals or organizations for using AI to generate content or perform tasks. AI shaming has emerged as a recent phenomenon in academia. This paper examines the characteristics, causes, and effects of AI shaming on academic writers and researchers. AI shaming often involves dismissing the validity or authenticity of AI-assisted work, suggesting that using AI is deceitful, lazy, or less valuable than human-only efforts. The paper identifies various profiles of individuals who engage in AI shaming, including traditionalists, technophobes, and elitists, and explores their motivations. The effects of AI shaming are multifaceted, ranging from inhibited technology adoption and stifled innovation to increased stress among researchers and missed opportunities for efficiency. These consequences may hinder academic progress and limit the potential benefits of AI in research and scholarship. Despite these challenges, the paper argues that academic writers and researchers should not be ashamed of using AI when done responsibly and ethically. By embracing AI as a tool to augment human capabilities and by being transparent about its use, academic writers and researchers can lead the way in demonstrating responsible AI integration.</p></div>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI Shaming: The Silent Stigma among Academic Writers and Researchers\",\"authors\":\"Louie Giray\",\"doi\":\"10.1007/s10439-024-03582-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>AI shaming refers to the practice of criticizing or looking down on individuals or organizations for using AI to generate content or perform tasks. AI shaming has emerged as a recent phenomenon in academia. This paper examines the characteristics, causes, and effects of AI shaming on academic writers and researchers. AI shaming often involves dismissing the validity or authenticity of AI-assisted work, suggesting that using AI is deceitful, lazy, or less valuable than human-only efforts. The paper identifies various profiles of individuals who engage in AI shaming, including traditionalists, technophobes, and elitists, and explores their motivations. The effects of AI shaming are multifaceted, ranging from inhibited technology adoption and stifled innovation to increased stress among researchers and missed opportunities for efficiency. These consequences may hinder academic progress and limit the potential benefits of AI in research and scholarship. Despite these challenges, the paper argues that academic writers and researchers should not be ashamed of using AI when done responsibly and ethically. By embracing AI as a tool to augment human capabilities and by being transparent about its use, academic writers and researchers can lead the way in demonstrating responsible AI integration.</p></div>\",\"PeriodicalId\":7986,\"journal\":{\"name\":\"Annals of Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10439-024-03582-1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10439-024-03582-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
AI Shaming: The Silent Stigma among Academic Writers and Researchers
AI shaming refers to the practice of criticizing or looking down on individuals or organizations for using AI to generate content or perform tasks. AI shaming has emerged as a recent phenomenon in academia. This paper examines the characteristics, causes, and effects of AI shaming on academic writers and researchers. AI shaming often involves dismissing the validity or authenticity of AI-assisted work, suggesting that using AI is deceitful, lazy, or less valuable than human-only efforts. The paper identifies various profiles of individuals who engage in AI shaming, including traditionalists, technophobes, and elitists, and explores their motivations. The effects of AI shaming are multifaceted, ranging from inhibited technology adoption and stifled innovation to increased stress among researchers and missed opportunities for efficiency. These consequences may hinder academic progress and limit the potential benefits of AI in research and scholarship. Despite these challenges, the paper argues that academic writers and researchers should not be ashamed of using AI when done responsibly and ethically. By embracing AI as a tool to augment human capabilities and by being transparent about its use, academic writers and researchers can lead the way in demonstrating responsible AI integration.
期刊介绍:
Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.