Class Data, Algorithms, and Artificial Intelligence

- Understanding the role and mechanisms of algorithms in AI - Analysing ethical issues, gender bias and other forms of bias (e.g. racial, socio-economic) in algorithmic decision-making, with a focus on human rights, privacy and the amplification of misinformation and its impacts on society - Identifying how data is collected and used in various fields, including the concepts of Data Colonialism and Surveillance Capitalism - Analyse data, algorithmic and artificial intelligence literacy in the context of reference frameworks, from a socio-technical perspective - Evaluate the regulation and ethical governance of AI based on international recommendations - Develop artificial intelligence governance plans aligned with best practices from predefined models, focusing on multi-stakeholder and collaborative perspectives applicable to various environments and industries

Not applicable

The programme content of the course covers data literacy, algorithms and AI, beginning with an: 1. Introduction to the importance of data and algorithms in the digital age, highlighting the critical role of literacy in these areas. 2. Explore ethical issues, such as gender, race and class biases embedded in algorithmic systems, and discuss their social consequences. 3. Analysing and evaluating, in a socio-technical approach, AI as an integrated ecosystem, where technology and society co-construct each other. 4. AI regulation is evaluated, with a focus on human rights and global policies, models, and regulations. The course also includes a critical analysis of AI strategies and the role of large technology companies in AI governance. Finally, 5. Students will participate in case studies and develop practical projects for AI governance plans, applying knowledge about data and AI in real contexts, with an ethical and responsible approach.

1. Expository: At the beginning of each module, the expository method will be used to introduce fundamental concepts about data, algorithms, and AI. The presentation of topics will highlight the importance of data literacy, algorithmics, artificial intelligence, and ethical principles in responsible use. Classes will be accompanied by slides, articles, and references, ensuring access to reliable and up-to-date sources; 2. Interrogative: The interrogative method will stimulate critical thinking, especially in the analysis of gender, race, and class biases in algorithmic systems (CP2), through questions that challenge students to reflect on the ethical implications of data collection and use; 3. Active: The active method will be applied in the practical modules (CP4-6), with students working in groups to solve case studies on ethical challenges in the implementation of AI in real contexts (CP3-4) and developing MIL projects and AI governance plans.

Couldry, N., & Mejias, U. A. (2018). Data colonialism: Rethinking big data's relation to the contemporary subject. Television & New Media, 20(4), 336-349. https://doi.org/10.1177/1527476418796632; Soares, T. T. N. G., & Santos, I. M. R. (2025). A Governança da Inteligência Artificial: Regulamentações e Práticas Éticas. Caderno Virtual, 1(62). https://www.portaldeperiodicos.idp.edu.br/cadernovirtual/article/view/8318 Fengchun, M., & Kelly, S. (2023). Guia para a IA generativa na educação e na pesquisa. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000390241; Kreinsen, M., & Schulz, S. (2023). Towards the triad of digital literacy, data literacy and AI literacy in teacher education. OSF Preprints. https://doi.org/10.35542/osf.io/xguzk; Newman, N. (2024). Journalism, media, and technology trends and predictions 2024. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-and-predictions-2024