urn:uuid:003350bd-c3d8-57cc-900c-22c43bde5825 New versions of this research opportunity and changes to the claims it rests on. 2026-10-18T00:00:00Z SocioLens urn:uuid:59231581-cc52-5e9b-b3ca-a7ad58e6c337 2026-10-18T00:00:00Z

When a language model answers a few European Social Survey Round 11 questions as Greek respondents of a given age, sex, education and region, do its answers come closer to or move further from those of the Greek respondents when the prompt is in Greek rather than in English, overall and across groups?

urn:uuid:e45e89a7-6dcd-5688-9188-341e77865a9f 2026-10-07T00:00:00Z

Pew Research Center (30 September 2026) built an AI “digital twin” for each panelist analyzed from three 2026 waves of its American Trends Panel, a nationally representative panel of randomly selected U.S. adults. Each twin was conditioned on that person’s profile and earlier survey answers, and weighted synthetic and human results were compared on nearly 300 questions. With its best-performing configuration (Claude Opus 4.6), synthetic estimates differed from human estimates by an average of 12.4 percentage points. Error was substantial for every subgroup reported, reaching 16.1 points for Republicans and Republican leaners and 15.1 points for Black adults. On 47% of questions at least one answer choice was selected by no synthetic respondent, which happened on no question among humans. Results also differed systematically by AI model, although some toplines matched closely. This strengthens the evidence for LD07-HQ-C06 on selected-average agreement, real-world response variance and subgroup heterogeneity. The report contains no regression or treatment-effect analysis and no individual-level accuracy result for its final configuration, so those parts of the claim are unchanged. The claim wording is unchanged.