Public Opinion on the Politics of AI Alignment: Cross-National Evidence on Expectations for AI Moderation From Germany and the United States

Recent advances in generative AI have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment—how well AI systems meet public expectations regarding safety, fairness, and social values. However, little is known about what people expect from AI-enabled systems and how these expectations differ across national contexts. We present evidence from two surveys of public preferences for key functional features of AI-enabled systems in Germany ( n = 1800) and the United States ( n = 1756). We examine support for four types of alignment in AI moderation: accuracy and reliability, safety, bias mitigation, and the promotion of aspirational imaginaries. U.S. respondents report significantly higher AI use and consistently greater support for all alignment features, reflecting broader technological openness and higher societal involvement with AI. In both countries, accuracy and safety enjoy the strongest support, while more normatively charged goals—like fairness and aspirational imaginaries—receive more cautious backing, particularly in Germany. We also explore how individual experience with AI, attitudes toward free speech, political ideology, partisan affiliation, and gender shape these preferences. AI use and free speech support explain more variation in Germany. In contrast, U.S. responses show greater attitudinal uniformity, suggesting that higher exposure to AI may consolidate public expectations. These findings contribute to debates on AI governance and cross-national variation in public preferences.

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Publication Details

Journal
Social Media + Society
Published
2025-10-01
DOI
https://doi.org/10.1177/20563051251405069
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Public Opinion on the Politics of AI Alignment: Cross-National Evidence on Expectations for AI Moderation From Germany and the United States

Social Media + Society
Ethics and Social Impacts of AI
article

Public Opinion on the Politics of AI Alignment: Cross-National Evidence on Expectations for AI Moderation From Germany and the United States

article en

Abstract

Recent advances in generative AI have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment—how well AI systems meet public expectations regarding safety, fairness, and social values. However, little is known about what people expect from AI-enabled systems and how these expectations differ across national contexts. We present evidence from two surveys of public preferences for key functional features of AI-enabled systems in Germany ( n = 1800) and the United States ( n = 1756). We examine support for four types of alignment in AI moderation: accuracy and reliability, safety, bias mitigation, and the promotion of aspirational imaginaries. U.S. respondents report significantly higher AI use and consistently greater support for all alignment features, reflecting broader technological openness and higher societal involvement with AI. In both countries, accuracy and safety enjoy the strongest support, while more normatively charged goals—like fairness and aspirational imaginaries—receive more cautious backing, particularly in Germany. We also explore how individual experience with AI, attitudes toward free speech, political ideology, partisan affiliation, and gender shape these preferences. AI use and free speech support explain more variation in Germany. In contrast, U.S. responses show greater attitudinal uniformity, suggesting that higher exposure to AI may consolidate public expectations. These findings contribute to debates on AI governance and cross-national variation in public preferences.

Social Media + SocietyVol. 11(4)
National Taiwan University (TW), University of Bamberg (DE)
Gender equality
Openalex Percentile: Top 35%
Ethics and Social Impacts of AI
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