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Lecturer(s)
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Franc Jaroslav, doc. Mgr. Th.D.
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Course content
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A case-based programme on algorithmic decision-making, social justice, critical discourse analysis and responsible public communication. Programme description The BIP brings together students and academics to critically examine the societal impact of AI-driven decision-making. Through a blended learning format, participants explore real-world cases in which algorithms have shaped or harmed individuals and communities, and investigate the political, social and theoretical conditions that may have enabled such harm. They develop interdisciplinary analytical skills, apply critical discourse analysis, create audiovisual accounts of specific cases, and propose ethical and practical approaches to responsible AI governance. Learning activities and assessment are aligned with selected competences from the LOUIS framework. The programme combines online preparation, five on-site teaching days, and online follow-up.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Activating (Simulations, Games, Dramatization), Group work
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Learning outcomes
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By the end of the programme, participants will be able to demonstrate the outcomes below. 1. Reconstruct a documented AI-related case, identifying the decision process, affected groups, claimed benefits, observed harms and gaps in the evidence. 2. Analyse how institutional incentives, political choices and assumptions about neutrality, efficiency and human behaviour influence AI deployment and its public justification. 3. Apply at least two ethical perspectives to a case, explain conflicting obligations and defend a judgement against a substantive objection.
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Prerequisites
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Designed for advanced undergraduate and postgraduate students across communication studies, humanities, social sciences, law, education and computing. Academics may participate as contributors or co-learners. Working language: English; recommended proficiency: B2 or equivalent. No programming experience is required. Participants should be willing to read academic texts and collaborate across disciplines and cultures.
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Assessment methods and criteria
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unspecified
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Recommended literature
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Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77?91..
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Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence..
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European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act)..
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Fairclough, N. (2003). Analysing Discourse. Textual analysis for social research. London - New York.
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Floridi, L., et al. (2018). AI4People?An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations, 28, 689?707..
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Nichols, B. (2017). Introduction to Documentary (3rd ed.)..
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Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press..
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Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of FAT* ?19, 59?68..
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UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence..
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