Aug 2026· Proceedings of the AAAI/ACM Conference on AI Ethics and Society· 0 citations
Computer Science
Abstract
As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern. A growing literature has leveraged large scale surveys to investigate to what extent LLMs'and humans'stated values and opinions align. With limited exceptions, studied populations have been defined country borders or cultural bounds. Yet, this focus neglects the role that socio-demographic divides may play for value alignment disparities. Relying on the European Social Survey, we address this knowledge gap by considering value alignment displayed with respect to 10 prominent commercial LLMs in terms of 15 socio-demographic variables as well as country of residence. Our analyses reveal that LLMs are indeed unequally aligned to the values of different socio-demographic groups, notably those defined by education, income, occupation and religion. When examining alignment at the individual level, a respondent's country, taken as a stand-alone variable, explains a substantial amount of variation that is on par with the full set of considered socio-demographics. Further disentangling the respective role of country-level and socio-demographic factors, we find they are complementary in explaining value alignment patterns, with their relative weights varying across the subset of questions considered.
While linguistic capital has been widely studied in Western labour markets (Thuesen, 2016; Brophy, 2011), its gendered returns in post-conflict economies – where language skills intersect with fragile institutions and patriarchal norms – remain critically under-researched (Matysiak et al., 2024). Drawing on human c...
Vjosa Hamiti, Albulena Shala, Era Hamiti· International journal of man...· 0 citations
Abstract This paper examines whether generational change in Central and Eastern Europe (CEE) is associated with shifts in how political attitudes align along the conventional economic–cultural left–right dimension. Because cohorts in the region were socialized under different political–economic systems, CEE provides a...
Lenka Hrbková, Tadeas Cely, Matej Jungwirth· Public Opinion Quarterly· 0 citations
This article examines the impact of migration, globalization and digitalization on the use of nominal address terms in the English‐speaking Sri Lankan Tamil (SLT) diaspora community by undertaking a corpus analysis of English fiction authored by diaspora members with SLT heritage. In the 40 years since the outbreak o...
Background and Aims: This conceptual review article examines why accusations of Western double standards have become a durable frame in the contestation of the liberal international order. Existing international-relations scholarship has explained this contestation mainly through changes in material power, norm diffusi...
Despite the expansion of non-contributory cash transfer programmes in Latin America – especially during the COVID-19 pandemic – public support for a universal basic income (UBI) remains remarkably low. Using original survey data from seven countries, we find that fewer than 10 per cent of respondents support both the...
Fabián A. Borges, Juliana Martínez Franzoni, Merike Blofield· Journal of Social Policy· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026