{"title":"Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling","authors":"Hassnian Ali, Ahmet Faruk Aysan","doi":"10.1108/ijoes-04-2024-0112","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.</p><!--/ Abstract__block -->\n<h3>Research limitations/implications</h3>\n<p>This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.</p><!--/ Abstract__block -->","PeriodicalId":42832,"journal":{"name":"International Journal of Ethics and Systems","volume":"42 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Ethics and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijoes-04-2024-0112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).
Design/methodology/approach
Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.
Findings
The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.
Research limitations/implications
This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.
Originality/value
The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.
期刊介绍:
The International Journal of Ethics and Systems (formerly named Humanomics, the International Journal of Systems and Ethics) is a multidisciplinary journal publishing peer review research on issues of ethics and morality affecting socio-scientific systems in epistemological perspectives. The journal covers diverse areas of a socio-scientific nature. The focus is on disseminating the theory and practice of morality and ethics as a system-oriented study defined by inter-causality between critical variables of given problems.