Degree | Type | Year |
---|---|---|
Artificial Intelligence | FB | 2 |
You can view this information at the end of this document.
No prerequisites
Drawing from real-world case studies, this course is designed to instill in students awareness of the ethical and societal implications of artificial intelligence (AI). It provides comprehensive instruction on incorporating strategies and utilizing tools to minimize ethical risks while fostering the development of AI systems within the framework of responsible AI.
Part I: Ethical Aspects of Artificial Intelligence
1. Introduction: Why should AI professionals study ethics
1.1. ACM Code of Ethics and Professional Conduct
1.2. Ethical frameworks (consequentialism, theory of justice, virtue ethics...)
1.3. Ethical principles (fairness, responsibility, justice, privacy...)
2. Data collection and privacy
2.1. The importance of privacy
2.2. Main techniques for data privacy (anonymity, encryption, differential privacy...)
2.3. Privacy beyond data (in context, by design...)
3. Algorithms, decision-making, and biases
3.1. Technical definitions of bias in algorithmic outcomes
3.2. Direct and indirect algorithmic discrimination
3.3. Definition of fairness and fairness metrics
3.4. Representation of normative and ethical knowledge in AI
3.5. Ethical guidelines for reliable AI: AI-Fairness Toolkits
4. Explainability
4.1. The impact on responsibility and accountability in autonomous systems, focusing onthe case of autonomous vehicles
4.2. The importance of good explanations in AI systems
4.3. Tools for evaluating explainability
Part II: Ethical-Political Aspects of Artificial Intelligence
5. Introduction: Why are the political and social aspects of artificial intelligence relevant?
5.1. Theory of technological mediation
5.2. Narrative around AIand technological determinism.
5.3. Responsible innovation and research (RRI)
6. Ethics and robotics
6.1.Robots and society
6.2. Ethical challenges in robotics
6.3. Applied examples of robotics in everyday life
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Case studies | 50 | 2 | 4, 14, 5, 10, 9, 11, 6 |
Lesson attendance and active participation | 30 | 1.2 | 14, 3, 5, 13, 1, 11, 12, 16 |
Practices and exercise | 50 | 2 | 2, 5, 8, 9, 13, 1, 12, 16, 7 |
The course's orientation is predominantly practical. Each class will typically commence with the presentation of a real-world case study, fostering a subsequent group discussion. Following that, concepts, methods, or AI systems related to the ethical concerns raised by the case will be introduced and explained. Finally, students will engage in individual or group practices to reinforce their learning of the lecture. In some classes, time will be kept for reviewing and correcting these practices. Few classes will consist of visits to AI research centers.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Evaluative task 1 | 34% | 7 | 0.28 | 4, 2, 14, 3, 5, 8, 10, 9, 13, 1, 11, 12, 16, 6, 7 |
Evaluative task 2 | 33% | 7 | 0.28 | 4, 14, 3, 5, 8, 10, 9, 13, 1, 16, 7 |
Evaluative task 3 | 33% | 6 | 0.24 | 3, 5, 13, 15, 11, 12, 16 |
The assessment of the course can be carried out through two modalities: continuous assessment or single assessment.
The student must individually complete three assessment activities: two corresponding to Part I and one to Part II.
To be eligible for this modality, all three activities must be completed. To pass the course, the student must obtain a minimum grade of 5 out of 10 in each activity. Otherwise, the student will have to take the resit exam. The final grade will be the arithmetic mean of the three assessment activities.
The student will take an individual final exam in January, structured in three parts equivalent to the activities of the continuous assessment, both in type and weight. To pass, the student must obtain a minimum grade of 5 out of 10 in each of the three parts. The final grade will be the arithmetic mean of the three scores.
Students eligible for the resit exam are:
The resit will consist of an individual final exam. To pass, a minimum grade of 5 is required. This will be the final grade for the course.
After each assessment activity, the teaching staff will inform students via Moodle about the grades obtained and the procedure and date for the review.
Honours Distinction
Honours distinctions will be awarded to students with a final grade of 10. If there are more students with this grade than the number of honours distinctions available for the course, an additional test will be held to determine the recipients.
The student will receive the qualification “Not assessable” if they do not attend more than one assessment activity (continuous assessment) or if they do not attend the January exam (single assessment).
No differentiated treatment is foreseen for repeat students.
In this course, the use of AI technologies is not permitted at any stage. Any work that includes AI-generated content will be considered a breach of academic integrity and may result in partial or total penalties on the activity grade, or more severe sanctions in serious cases.
Any irregularity that may significantly alter the grade of an activity will result in a zero for that activity. In the case of multiple irregularities, the final grade for the course will be zero, regardless of any disciplinary proceedings.
If tests or exams cannot be held in person, they will be adapted to an online format made available through the UAB’s virtual tools (the original weighting will be maintained). Homework, activities, and class participation will be carried out via forums, wikis, and/or discussions on Teams, etc. The teaching staff will ensure that students can access these virtual tools or will offer feasible alternatives.
To be determined (Part I).
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 711 | English | first semester | afternoon |
(TE) Theory | 71 | English | first semester | afternoon |