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<p>This study examines public perceptions of AI fairness across three societal contexts&#xa0;in the U.S.: personal life, work life, and public life. AI fairness is conceptualized through perceived&#xa0;harms and benefits, offering a nuanced perspective on how individuals assess AI's impact on themselves versus others. Findings&#xa0;show that AI is generally perceived as more beneficial in personal and work contexts, while public life elicits greater skepticism and concern about harm. Women consistently perceive AI as less beneficial and more harmful across all contexts compared to men, reflecting broader gendered concerns about algorithmic decision-making Ethnicity differences also emerge, with Hispanic and Other minority groups (including Asian, Indigenous, and Native American individuals) reporting higher perceived benefits from AI. Conversely, White and Black participants are more likely to view AI as harmful across the&#xa0;various societal&#xa0;contexts. These disparities highlight&#xa0;the need for AI development and policy frameworks that account for demographic differences in perception to ensure equitable access and benefits across diverse populations.&#xa0;By integrating public perspectives into AI governance, this study aims to show how inclusive AI design can mitigate systemic bias, improve accessibility, and ensure AI-driven opportunities are equitably distributed rather than deepening technological disparities.&#xa0; .</p>

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 Who Benefits from AI? Examining Different Demographics' Fairness Perceptions across Personal, Work, and Public Life

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Sejin Paik,
  • Ekaterina Novozhilova,
  • Kate K. Mays,
  • James E. Katz

摘要

This study examines public perceptions of AI fairness across three societal contexts in the U.S.: personal life, work life, and public life. AI fairness is conceptualized through perceived harms and benefits, offering a nuanced perspective on how individuals assess AI's impact on themselves versus others. Findings show that AI is generally perceived as more beneficial in personal and work contexts, while public life elicits greater skepticism and concern about harm. Women consistently perceive AI as less beneficial and more harmful across all contexts compared to men, reflecting broader gendered concerns about algorithmic decision-making Ethnicity differences also emerge, with Hispanic and Other minority groups (including Asian, Indigenous, and Native American individuals) reporting higher perceived benefits from AI. Conversely, White and Black participants are more likely to view AI as harmful across the various societal contexts. These disparities highlight the need for AI development and policy frameworks that account for demographic differences in perception to ensure equitable access and benefits across diverse populations. By integrating public perspectives into AI governance, this study aims to show how inclusive AI design can mitigate systemic bias, improve accessibility, and ensure AI-driven opportunities are equitably distributed rather than deepening technological disparities.  .