This course offers the opportunity to discuss the development and deployment of artificial intelligence, alongside its societal impacts and ethical challenges. The course will then focus on critical issues within AI systems, such as algorithmic bias, discrimination, and inequality.

Upon successful completion of this course, students are expected to achieve the following: Knowledge and understanding The course will provide key theoretical tools to understand the development and deployment of artificial intelligence. Students will become familiar with the core principles of AI ethics—such as fairness, transparency, and accountability—alongside foundational theories of ethical action as they apply to algorithmic systems. Applying knowledge and understanding Students will be able to write argumentative analyses of real-world case studies in AI ethics (e.g., algorithmic bias, autonomous decision-making, and surveillance). They will learn to practically apply ethical theories and decision-making procedures to complex technological scenarios. Making judgements Students are expected to analyze complex ethical dilemmas surrounding AI and demonstrate an in-depth, critical understanding of the scope and challenges of AI governance. This includes evaluating the efficacy of regulations and public policies currently in place or anticipated in the near future. Communication Skills This course will enable students to master key terminology at the intersection of technology and philosophy, allowing them to communicate their ideas, proposals, analyses, and critical reasoning regarding AI effectively and appropriately. Students will also be able to verbally articulate coherent, ethically grounded arguments to their peers. Learning skills This course will empower learners by providing the tools to determine why certain AI guidelines, public policies, or ethical frameworks are adopted while others are not. Furthermore, students will be equipped to independently evaluate explanatory models and critically assess the ongoing, rapid developments in the field of artificial intelligence.

Module 1: Foundations of AI and Digital Ethics This introductory module establishes the baseline understanding of both artificial intelligence technologies and moral philosophy. Demystifying AI: What AI is (and is not), an overview of machine learning, neural networks, and how data trains algorithms. Theories of Ethical Action: An introduction to classical ethical frameworks (Utilitarianism, Deontology, Virtue Ethics) and how they apply to digital environments and non-human agents. The Emergence of AI Ethics: Historical context, the shift from computer ethics to AI ethics, and why AI presents unique moral challenges compared to previous technologies. Module 2: Algorithmic Bias, Discrimination, and Fairness This module focuses on how human prejudices and historical inequalities can be encoded into AI systems. The Anatomy of Bias: How biased training data and algorithmic design lead to discriminatory outcomes. Case Studies in Inequity: Examining real-world failures in facial recognition, predictive policing, hiring algorithms, and healthcare diagnostics. Defining Fairness: The tension between mathematical definitions of fairness and social justice. Module 3: Transparency, Explainability, and Accountability This section explores the "Black Box" problem and the challenge of attributing moral responsibility when AI systems make autonomous decisions. The Black Box Problem: The technical and ethical challenges of opaque algorithms where the decision-making process is hidden from the user. Explainable AI (XAI): The right to an explanation and the technical efforts to make AI systems interpretable. Moral and Legal Responsibility: Who is to blame when an AI fails? (e.g., autonomous vehicle accidents, automated financial errors). Module 4: Privacy, Autonomy, and Surveillance This module examines how AI impacts individual rights and societal structures through data collection and monitoring. Data Harvesting and Consent: The ethics of big data, data scraping, and the erosion of privacy in the digital age. Surveillance Capitalism and State Monitoring: The ethical implications of mass surveillance, biometric tracking, and social credit systems. Manipulation and Deepfakes: Generative AI, truth, misinformation, and the impact of hyper-personalized algorithmic feeds on human autonomy and democracy. Module 5: AI Governance, Regulation, and Public Policy Building on the previous modules, students will analyze how society can and should regulate AI technologies. Corporate Self-Regulation vs. Public Policy: Evaluating the effectiveness of "AI for Good" initiatives, corporate ethics boards, and internal guidelines. Global Regulatory Frameworks: A critical analysis of major public policies, with a strong focus on the EU AI Act, the GDPR, and comparative approaches in other global jurisdictions. Auditing AI: Tools, impact assessments, and frameworks for ensuring compliance and ethical deployment in the real world. Module 6: The Future of AI and Societal Impact The final module looks ahead to the long-term, systemic impacts of artificial intelligence on human life. Labor and Inequality: The automation of cognitive and physical work, the gig economy, and the potential exacerbation of global economic disparities. Human-AI Interaction: The ethics of anthropomorphizing AI, chatbots, and AI companions. Existential and Long-Term Risks: Artificial General Intelligence (AGI), the alignment problem (ensuring AI goals align with human values), and the long-term stewardship of AI.

Turing A. M. (1950). Computing Machinery and Intelligence; in Mind, 49: 433-460. Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Dubber M.D., Pasquale F., & Das S. (Eds.), The Oxford Handbook of AI Ethics, Oxford University Press (2020). Leben D. (2025). AI Fairness: Designing Equal Opportunity Algorithms, MIT Press Bennett M. (2023). A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains, Mariner Books. Shadbolt N., & Hampson R. (2024). As If Human: Ethics and Artificial Intelligence, Yale University Press. Crawford K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, Yale University Press. Hao K. (2025). Empire of AI, Dreams and Nightmares in Sam Altman's OpenAI, Penguin Press. Yudkowsky E., Soares N. (2025). If Anyone Builds It, Everyone Dies. Why superhuman AI would kill us all, Little, Brown and Company. Golumbia D. (2024). Cyberlibertarianism: The Right-Wing Politics of Digital Technology, University of Minnesota Press

The course combines dialogic lectures, case analysis, seminar discussion on texts and work activities in small groups. The lessons introduce the concepts and theoretical models, while the case studies and business materials (codes, reports, policies) are used to develop the ability to apply and make critical judgments. Guided readings, short written analysis exercises and moments of discussion in the classroom are planned, in order to encourage active and reflective learning, consistent with the placement of the course in the area of humanities.

The assessment of learning includes: active participation in lessons and discussions (formative evaluation, not constrained, but considered in the overall judgment); For attending students: Continuous assessment: watching a videocast ahead of each lesson (certified via MyLuiss) and Midterm exam (writing a 500 words short essay): 30%; Final examination: 70%. a final oral exam (or, alternatively, written with open or multiple questions), focused on the understanding of the fundamental concepts, their integration and the ability to argue independently. For non attending students: a final oral exam (or, alternatively, written with open or multiple questions), focused on the understanding of the fundamental concepts, their integration and the ability to argue independently. Details on format, length of the short paper and evaluation criteria will be communicated at the beginning of the course and reported on the university platform.

Any assignment of final papers (three-year theses) related to the course will privilege: consistency of the proposed theme with the contents of Ethics of AI (corporate responsibility, governance, work, consumption, sustainability, ethics of technologies); clarity of the demand for research and feasibility of the work on schedule; willingness to integrate economic-managerial perspective and ethical-humanistic reflection; aptitude for bibliographic research and critical analysis of cases, documents and empirical data.