Scholarlink

<p>The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. Despite growing adoption across sectors, little is known about how corporate governance frameworks address these multifaceted risks in a comparative, sector-specific manner—a clear knowledge gaps this study fills. This study conducts a text-based analysis of 160 guidelines and policy statements across 14 industrial sectors, applying TF-IDF and K-Means clustering to identify thematic patterns and governance priorities. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. Key findings show marked divergence in focus areas—such as “transparency” in tech, “consent” in healthcare, and “IP protections” in publishing—with implications for designing adaptable, context-aware policy frameworks. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Generative AI and LLMs in industry: a text-mining analysis and critical evaluation of guidelines and policy statements across 14 industrial sectors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

查看全文
  • Junfeng Jiao,
  • Saleh Afroogh,
  • Kevin Chen,
  • David Atkinson,
  • Amit Dhurandhar

摘要

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. Despite growing adoption across sectors, little is known about how corporate governance frameworks address these multifaceted risks in a comparative, sector-specific manner—a clear knowledge gaps this study fills. This study conducts a text-based analysis of 160 guidelines and policy statements across 14 industrial sectors, applying TF-IDF and K-Means clustering to identify thematic patterns and governance priorities. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. Key findings show marked divergence in focus areas—such as “transparency” in tech, “consent” in healthcare, and “IP protections” in publishing—with implications for designing adaptable, context-aware policy frameworks. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.