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

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. .

Authors

Institutions

Publication Details

Journal
Discover Artificial Intelligence
Published
2025-04-24
DOI
https://doi.org/10.1007/s44163-025-00235-7
Citations
9
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
21.29
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Sejin Paik, James Everett Katz, Kate K. Mays, Ekaterina Novozhilova
9 citations
Discover Artificial Intelligence
Ethics and Social Impacts of AI
21.29
article

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

Sejin Paik, James Everett Katz, Kate K. Mays, Ekaterina Novozhilova
article en
9 citations

Abstract

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. .

Discover Artificial IntelligenceVol. 5(1)
Boston University (US), University of Vermont (US), Public Policy Institute of California (US), Georgetown University (US)
Openalex Percentile: Top 1%
Ethics and Social Impacts of AI
21.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.