Scholarlink

<p>This study investigates whether AI-powered Low-Code/No-Code (LCNC) solutions may unintentionally generate gender-biased responses. We developed four AI-powered LCNC implementations (i.e., Spreadsheet-based, Workflow-based, Web-Application-based and Mobile-Application-based), using different generative AI models, including those from OpenAI, DeepSeek, Claude, and Google DeepMind, and evaluated their outputs in response to prompts designed to highlight potential gendered associations in roles, traits, and personal preferences. Our analysis consists of two parts. First, we applied a mixed-methods structured content analysis to systematically identify potential stereotypical patterns in the responses of the AI models. Second, we compared the outputs across the different AI models for each prompt to explore variations in gender bias-related behavior. Our findings raise an ethical concern: without appropriate policies and guidelines in place, AI-powered LCNC solutions may replicate or even amplify existing societal biases. This work contributes to ongoing discussions on responsible AI integration and bias-aware design, especially within the evolving LCNC ecosystem.</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

AI-powered LCNC implementations and gender: a comparative study of role attribution bias

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

查看全文
  • Spyridon Tsoukalas,
  • Dialekti Athina Voutyrakou,
  • Marios Karelis,
  • Constantine Skordoulis,
  • Gianna Katsiampoura,
  • Markos Avlonitis,
  • Patrick Mikalef

摘要

This study investigates whether AI-powered Low-Code/No-Code (LCNC) solutions may unintentionally generate gender-biased responses. We developed four AI-powered LCNC implementations (i.e., Spreadsheet-based, Workflow-based, Web-Application-based and Mobile-Application-based), using different generative AI models, including those from OpenAI, DeepSeek, Claude, and Google DeepMind, and evaluated their outputs in response to prompts designed to highlight potential gendered associations in roles, traits, and personal preferences. Our analysis consists of two parts. First, we applied a mixed-methods structured content analysis to systematically identify potential stereotypical patterns in the responses of the AI models. Second, we compared the outputs across the different AI models for each prompt to explore variations in gender bias-related behavior. Our findings raise an ethical concern: without appropriate policies and guidelines in place, AI-powered LCNC solutions may replicate or even amplify existing societal biases. This work contributes to ongoing discussions on responsible AI integration and bias-aware design, especially within the evolving LCNC ecosystem.