Social Classes in The Digital Age

Using systematic empirical evidence on the main transformations affecting the occupational structure, the organization of work, the production process, the changes in tasks in demand, etc. in Europe, we will:

• Derive implications for the relevance of social classes as a social cleavage vis-à-vis other cleavages.
• Establish hypotheses about what these transformations imply for existing class categorisations (where are the blind spots?).
• Establish hypotheses about what the mechanisms at work are between social classes in the digital era and life chances (education, health, social mobility, political attitudes, wages, income and wealth).
• Describe the social structure in the EU as a whole and in each country using existing class schemas (how well accounted for is it?).
• Explore the necessity of creating a new class schema for the digital age and, if so, assess its predictive power.

Fair access to the labour market is crucial to maximising talent expression in jobs, avoiding discrimination and implicit biases against certain social groups and promoting social diversity in economic sectors, occupations and jobs. In this line we will:

• Assess biases in AI-led hiring processes.
• Benchmark those vis-à-vis conventional recruiting methods.
• Make recommendations on how to raise awareness on the existence of these biases and their consequences and to minimize their negative effects when designing algorithms.

The socioeconomic position is strongly correlated with various socially desirable outcomes, such as health. We will address three topics:

• The use of novel technologies (machine learning, neural networks) to predict health outcomes vis-à-vis conventional statistical methods and to predict risk assessment.
• The study of whether eugenics or social Darwinism are a potential implication of access to Medically Assisted Reproduction technologies if (a) access presents a social gradient; and (b) babies born via MAR show superior health and cognitive outcomes.
• The empirical assessment of the effect of parental socioeconomic resources on infant health.

Families with more socioeconomic resources are more capable of passing on their advantages to their offspring than those families with fewer resources. This intergenerational transmission of (dis)advantages is a threat to social inclusion and social mobility.

In this line of research, we will focus on three main relevant aspects:

• Teachers’ (implicit) biases against certain social groups. We will assess biases in AI-led assessment/marking processes, will benchmark those vis-à-vis conventional methods, and will make recommendations on how to raise awareness on the existence of these biases and their consequences and to minimize their effects.
• Design educational interventions involving technology and assess their potential to improve learning outcomes and child wellbeing vis-à-vis conventional (face-to-face) teaching.
• Design educational interventions involving technology and assess their potential to generate more equal benefits across different groups (parental background, gender, special needs).

One of the potential negative implications of technological change is a threat to the existing social contract due to increased discontent from the “losers” of such transformations. In this line we will:

• Explore whether political attitudes, demands for redistribution, dissatisfaction with institutions, etc. have been substantially altered as a consequence of technological change.
• Determine what novel needs, in terms of social protection, would be required in a European new social contract.
• Determine citizens’ support for a renewed social contract.