1. Artificial Intelligence: Definition of AI. What it is and how it works, with examples of its application in social contexts. Sociological theories on technological innovation and social change. The impact of AI on social structures and power dynamics.
2. AI and Social Inequalities: Analysis of algorithmic biases and implicit discrimination in AI systems; discussion on AI and new forms of social exclusion (e.g., access to services, automated discrimination). Case studies: algorithmic profiling, social credit systems, predictive justice.
3. Ethical and Regulatory Aspects of AI: Analysis of the ethical implications of AI use, including privacy, surveillance, and algorithmic transparency; emerging regulations and a discussion on AI governance policies at national and international levels.

1. Artificial Intelligence and Social Change
Specific objectives:
 • Understand the basic principles of AI and its applications in social contexts.
 • Examine the interaction between technological innovation and social change, with a focus on sociological theories.
Content: • Definition and functioning of AI (Machine Learning, Deep Learning, Big Data).
 • Examples of AI applications: education, public health, social policies.
 • Sociological theories of technological innovation (e.g., Rogers, Castells, Latour) and their application to social changes.
 • The impact of AI on social structures and power dynamics.
2. AI and Social Inequalities
Specific objectives:
 • Analyse the inequalities created or amplified by AI systems.
 • Recognise algorithmic biases and implicit discrimination in automated systems.
Content:
 • Algorithmic bias: how it arises and why it is problematic.
 • Critical analysis of algorithmic profiling (e.g., facial recognition, social scoring).
 • Social implications: exclusion from essential services, automated discrimination.
 • Case studies: predictive justice, China’s social credit system, discrimination in job access.
3. Ethical Aspects of AI
Specific objectives:
 • Understand the ethical implications of using AI in collective decision-making.
 • Reflect on issues of privacy, transparency, and surveillance.
Content:
 • Privacy and control: the handling of personal data in AI systems.
 • Opaque algorithms and transparency (the “black box”).
 • Algorithmic surveillance and its impacts on individual and collective freedoms.
 • Ethical responsibility: who is accountable for AI errors?