What factors explain majority members’ anti-Muslim prejudice? This is an increasingly important question to ask, but to date only relatively few studies have sought to provide answers from a cross-national comparative perspective. This study aims to help fill this gap. Using data from the seventh round of the European Social Survey (ESS) linked with country-level characteristics, our results indicate that (a) a larger Muslim population size, (b) more liberal immigrant integration policies and (c) greater state support of religion are all associated with lower levels of majority members’ negative attitudes towards Muslim immigration – our indicator of anti-Muslim prejudice. Such attitudes, however, prove to be unrelated to (d) cross-national differences in the frequency of negative immigration-related news reports as measured by the ESS media claims data. Collectively, these findings bring us one important step closer towards a better understanding of interethnic relations between majority members and Muslim immigrants in European host societies.

The extent to which ethnic segregation results from differences in socio‐economic factors remains a seminal topic of debate. The growing literature demonstrating the multifaceted phenomenon of segregation urges more focus on individuals’ spatial and social interactions. We applied an activity space approach and considered ethnic differences in individuals’ activity spaces as an indicator of spatial segregation. We used mobile phone and survey datasets in Estonia. We show that place‐based segregation indices derived from both datasets indicate similar levels of ethnic segregation. From an activity space perspective, the results show that the main socio‐economic factor affecting the extensity of activity spaces is self‐estimated social status rather than education and income. Results show that ethnic inequality in spatial behaviour is not straightforward, but rather that it is linked to how individuals position themselves in society. We argue that socio‐economic factors need to be controlled to examine ethnic segregation from activity space perspective.

This study explores the differences and similarities between the perceptions of data experts and refugees as data subjects, in the context of a refugee relocation algorithm. The study conducted in-depth interviews with data experts and Syrian refugees in Estonia and Turkey. The results indicate that both refugees and data experts acknowledge the algorithms’ potential power for structuring the everyday life experiences of people. Whereas refugees mainly focused on cultural and social concerns, the data experts underlined the importance of refugees’ agency and the potential drawbacks of algorithms in terms of transparency and accountability. While both groups of interviewees thought the relocation algorithm could be useful especially in economic terms, the study demonstrates that algorithms create complex power relations and place extra pressure on both refugees and data experts. The new digital landscapes produced by algorithms entail a ‘triple agency’ – an agency of experts developing and using these datafied solutions, an agency of data subjects being targets of those calculations, and an agency of algorithms. For solving the issue of ‘false authority’, where the modelling of spatial choice cannot grasp the socio-cultural reality, it is necessary to consider the socio-cultural context of the calculative devices. A paradigm shift in machine learning is necessary from learning machines as autonomous subjects to machines learning from social contexts and individuals’ experiences. Rather than experimenting with algorithmic solutions to speed up decisions about human lives, migration policies and relevant datafied solutions should consider the diversity of human experiences expressed in individuals’ everyday life.

This article focuses on generational differences in spatial mobility. Assuming that the ability to cope with the social transformations related to growing mobility varies significantly across generations, we use mobile positioning data collected in Estonia during 2014 providing four main indicators, namely, the number of locations visited and the distances between visited locations, within Estonia and abroad. The results indicate that spatial mobility declines linearly with age; however, a high degree of heterogeneity exists within age groups. Whereas the spatial mobility of the most active members of the younger generation takes place mostly within Estonia, among the most active older generation focus their activity beyond its borders. The study reveals “delayed mobility” patterns among the most active groups of the older generation and a new “immobility culture” among the younger generation in terms of cross‐border activities in a transition society.

Although algorithms are increasingly used for enabling the automation of tasks in public administration of welfare states, the citizens’ knowledge of, experiences with and attitudes towards automated decision-making (ADM) in public administration are still less known. This article strives to reveal the perspectives of citizens who are increasingly exposed to ADM systems, relying on a comparative analysis of a representative survey conducted in Estonia, Germany, and Sweden. The findings show that there are important differences between the three countries when it comes to awareness, trust, and perceived suitability of ADM in public administration, which map onto historical differences in welfare provisions or so-called welfare regimes.

Increasing attention is paid to ethical issues and values when designing and deploying artificial intelligence (AI). However, we do not know how those values are embedded in artificial artefacts or how relevant they are to the population exposed to and interacting with AI applications. Based on literature engaging with ethical principles and moral values in AI, we designed an original survey instrument, including 15 value components, to estimate the importance of these values to people in the general population. The article is based on representative surveys conducted in Estonia, Germany, and Sweden (n = 4501), which have varying experiences with implementing AI. The factor analysis showed four underlying dimensions of values embedded in the design and use of AI: (1) protection of personal interests to ensure social benefit, (2) general monitoring to ensure universal solidarity, (3) ensuring social diversity and social sustainability, and (4) efficiency. We found that value types can be ordered along the two dimensions of resources and change. The comparison between countries revealed that some dimensions, like social diversity and sustainability evaluations, are more universally valued among individuals, countries, and domains. Based on our analysis, we suggest a need and a framework for developing basic values in AI.