Ethical AI

Course Objective

Artificial intelligence is reshaping every sector of contemporary life. This course equips students with the philosophical foundations and the technical frameworks needed to evaluate, design, and deploy AI systems responsibly. Through two complementary blocks, Philosophical Foundations and Ethical AI in Computer Science, students progress from first‑principles reflection to hands‑on ethical analysis and system design.

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.

Course Content

The course consists of two separate yet complementary blocks:

Block I: Philosophical Foundations (Dr. Guido Löhr)

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. You will be able to see for yourself which debates are] exaggerated, and which debates should be paid more attention to. 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. Some of these topics are old, but recent progress in AI research (e.g., transformer models like GPT or LaMDA) also raises new philosophical issues.

Block II: Ethical AI in Computer Science (Dr. Elena Beretta)

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. Special attention will be given to the role of data, model behavior, and user interaction in shaping ethical outcomes, as well as strategies for designing AI systems that reflect socially responsible values.

Additional Information Teaching Methods

Teaching Methods

Interactive lectures, supervised group work, tutorials and exercises, project-based learning and peer feedback.

Use of Generative AI

Level 2b: Use of generative AI is permitted in a limited way, with disclosure

In this course, generative AI (GenAI), such as ChatGPT or similar tools, may be used in a limited way, for example for editorial support, rephrasing, or structure. Substantive contributions from GenAI may only be used within the limits set by the lecturer. Any use of GenAI must always be explicitly disclosed, in accordance with the lecturer's instructions. The student remains fully responsible for the content, quality, and accuracy of the submitted work. Incorrect use of GenAI is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.

Method of Assessment

The course includes three graded components: a multiple-choice exam, weekly assignments, and a final group project. Each element is designed to evaluate individual understanding and collaborative application of ethical AI principles in both philosophical and technical contexts.

Multiple-Choice Exam (50%)

A multiple-choice exam will be held to verify individual understanding of core concepts. This exam must be passed with a 5.5 or higher in order to complete the course successfully.

Homework Assignments (25%)

Students will complete 4 short individual homework assignments throughout the module. These assignments are designed to evaluate critical engagement with the course materials and case studies. Submissions will be evaluated by Teaching Assistants (TAs) based on a standardized grading rubric. To accommodate unexpected personal circumstances, only the 3 highest assignment grades will count toward the final grade (the lowest grade will be automatically dropped). Consequently, no extensions will be granted under any circumstances, and late and/or missed submissions will receive a grade of zero for that specific assignment. Assignments cannot be resubmitted.

Laboratory Session (25%)

Students will participate in regular practical laboratory sessions managed by Teaching Assistants (TAs). These sessions focus on hands-on application of ethical AI frameworks and algorithmic auditing tools.

Attendance at all laboratory sessions is mandatory. Labs are evaluated on a pass/fail basis. To receive a pass (full credit for the session), students must arrive on time, actively participate, and successfully complete the practical task assigned during the laboratory hour. Missed laboratory sessions or failure to complete the session task will result in a grade of zero for that specific lab. There are no opportunities to make up for missed laboratory sessions.