| Code |
16689
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| Year |
3
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
TP(60H)
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| Scientific area |
Philosophy
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Entry requirements |
N/A
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Learning outcomes |
By the end of the course unit, students should be able to:
1.Define, problematize, and interrelate the various concepts studied, analysing their historicity and complexity within the context of Artificial Intelligence (AI);
2.Understand and interpret the cultural and philosophical foundations of Artificial Intelligence;
3.Analyse and relate the ethical contributions of the philosophers studied within the framework of the Digital Age, Transhumanism, Late Capitalism, and Postmodernity;
4.Critically relate Artificial Intelligence, techne, technology, and the environment;
5.Critically relate Simulacra, Machines and Women;
6.Critically recognise the risks, challenges, and dilemmas associated with privacy and surveillance, automation and labour;
7.Discuss regimes of moral, legal, and algorithmic responsibility;
8.Understand regulatory frameworks and identify key principles of the General Data Protection Regulation (GDPR) and the European Union Artificial Intelligence Act (AI Act).
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Syllabus |
1.Definition of the concepts of Ethics, Privacy, Responsibility, and Trustworthiness, their interrelations, historicity, and relevance to Artificial Intelligence. The emergence of Applied Ethics;
2.WHO A(M) I? Cultural and philosophical foundations of Artificial Intelligence. The myths of Pygmalion and Daedalus. Relations between image, agalmatophilia, and Artificial Intelligence;
3.WHERE A(M) I? Cultural and sociopolitical contexts of Artificial Intelligence: Transhumanism, Postmodernity, the Digital Era, Late Capitalism, and Cyberspace.
4.Artificial Intelligence, Technology, and the Environment. Aristotle, Martin Heidegger, Hans Jonas, and Bruno Latour;
5.Simulacra, Machines, and Women. Donna Haraway and A Cyborg Manifesto;
6.The moral status of machines: moral versus legal responsibility. Automation and labour. Surveillance and privacy. Algorithmic accountability. Explainable AI. GDPR. EU Artificial Intelligence Act.
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Main Bibliography |
- ARISTÓTELES, Ética a Nicómaco. Quetzal, 2024.
- CLARK, A. e TORIBIO, J. (Eds.), Machine Intelligence: Perspectives on the Computational Model. Routledge, 1998.
- HARAWAY, D., A Cyborg Manifesto: Science, Technology, and Socialist-Feminism. University of Minnesota Press, 2016.
- HEIDEGGER, M., The Question Concerning Technology, and Other Essays. Garland Publishing, 1977.
- JONAS, H., The Imperative of Responsibility: In Search of an Ethics for the Technological Age. University of Chicago Press, 1985.
- LATOUR, B., Jamais Fomos Modernos: Ensaio de Antropologia Simétrica. Editora 34, 1994.
- STOICHITA, V., O Efeito Pigmalião: Para uma Antropologia Histórica dos Simulacros. KKYM, 2012.
- VALLOR, S., The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking. Oxford University Press, 2024.
- ZUBOFF, S., The Age of Surveillance Capitalism. Profile Books Ltd, 2019.
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Teaching Methodologies and Assessment Criteria |
Teaching/Learning Assessment (TLA): students are required to complete a midterm test and a written assignment. TLA also considers classroom performance (participation and attendance).
WA – Written Assignment: 45%
MT – Midterm Test: 45%
CP – Classroom Performance: 10%
The final course grade results from the weighted average of the grades obtained in the defined assessment components. Students pass the course and are exempt from the final exam if they obtain a final grade equal to or higher than 9.5/20.
Exam-based assessment (100%): written exam taken on UBI letterheaded paper, without consultation of any materials.
Requirements for eligibility to continuous assessment and the exam:
– minimum attendance of 70% during the teaching/learning period (except students with special status);
– minimum grade of 6/20 in TLA, where TLA = (WA × 45%) + (MT × 45%) + (CP × 10%).
Failure to meet these requirements results in failure in the course and ineligibility to take the exam.
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Language |
Portuguese. Tutorial support is available in English.
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