Ethical AI
Philosophical foundations and technical frameworks for responsible AI: moral agency, fairness, transparency, accountability, and the EU AI Act.
Learning objectives
Knowledge and insight: Understand foundational philosophical texts and theories relevant to artificial intelligence (e.g., moral agency, consciousness, language); Demonstrate knowledge of practical ethical AI frameworks, methodologies, and principles used in computer science, including fairness metrics, transparency techniques, and accountability standards. Applying knowledge and insight: Place contemporary debates on artificial intelligence within its proper historical and philosophical contexts; Apply key philosophical theories relevant to assessing the ethical relevance of cutting-edge AI technologies; Utilize computer science tools and ethical frameworks practically to assess, design, and implement ethical AI systems in real-world scenarios. Judgement: Analyze real-world data and AI system outputs critically, assessing ethical dimensions and technical considerations; Formulate informed ethical judgments about AI systems by integrating empirical evidence, philosophical perspectives, and established ethical standards. Communication: The student can clearly structure and express own philosophical views in writing and speech; Communicate technical insights and ethical considerations effectively. Learning skills: Relativize and contextualize potential threats posed by AI but also identify threats that might be overlooked; Continuously update ethical assessments in light of new technologies and evidence.
The course consists of two separate yet complementary blocks:
Block I: Philosophical Foundations — Everyone seems to be talking about AI these days. While some present AI as the savior of humanity, others claim that it will soon become conscious and inevitably destroy us. In this course, we will provide the philosophical background that will allow you to contextualize the most important academic and non-academic debates about AI. We will start with some basic ethical issues (responsible AI, moral agency of AI). Those discussions will soon motivate more theoretical or foundational topics from the philosophy of mind and language.
Block II: Ethical AI in Computer Science — This block focuses on the practical implementation of ethical principles in computer science and AI system development. Students will engage with key concepts such as fairness, transparency, and accountability, examining how these values can be operationalized through technical methods and design practices. Through hands-on exercises and real-world case studies, students will learn to identify ethical challenges in AI systems and apply contemporary frameworks and tools to address them.
Assessment
Multiple-Choice Exam (40%) — must be passed with a 5.5 or higher. Weekly Assignments (10%) — some pass/fail, others numerically graded; late/missed submissions receive zero. Final Group Project (50%) — includes written report and group presentation; must obtain 5.5 or higher to pass.
Teaching methods
Interactive lectures, supervised group work, tutorials and exercises, project-based learning and peer feedback.
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Ethical AI
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