Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling

IF 1.7 Q2 ECONOMICS
Hassnian Ali, Ahmet Faruk Aysan
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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.

生成式人工智能的伦理维度:利用机器学习结构主题建模的跨领域分析
本研究的目的是全面研究围绕生成式人工智能(AI)的伦理影响。本研究采用了一种新颖的方法论,从 Scopus 收集了 364 篇文档,时间跨度为 2022 年至 2024 年。利用词频-反向文档频率(TF-IDF)和结构主题建模(STM),该研究定量剖析了教育、医疗保健、商业和科学研究等不同领域的生成式人工智能伦理讨论的主题本质。在学术界,主要焦点是真实性和知识产权问题,突出了人工智能生成的内容在维护学术诚信方面的挑战。在医疗保健领域,重点转向人工智能在医疗决策和患者隐私方面的伦理影响,反映了人们对人工智能生成的医疗建议的可靠性和安全性的担忧。本研究还揭示了教育和金融领域的重要伦理讨论,显示了生成式人工智能对社会和专业实践的广泛影响。研究局限/影响本研究通过使用 STM 进行系统分析,为制定有针对性的生成式人工智能伦理指南和法规奠定了基础。它强调了对人工智能不断演变的伦理环境进行动态治理和持续监控的必要性,为不同领域的未来研究和政策制定提供了一个范例。原创性/价值本研究引入了 TF-IDF 和 STM 的独特方法组合,对大型学术语料库进行分析,为了解生成式人工智能在多个领域的伦理影响提供了新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.40
自引率
11.10%
发文量
67
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.
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