|
On successful completion of this course, students:
- O1. familiarize with the state-of-the-art research on the ethics of data science
- O2. define, describe and recall basic concepts and principles underlying responsible data science
- O3. identify stakeholders and ethical implications of data science in healthcare, design, crime, education, science, job markets, business, journalism, etc
- O4. understand ethical implications of data science algorithms on privacy, surveillance, discrimination, access to information, security, free will, human rights, social norms, etc
- O5. write an essay and a critical review in the field of responsible data science.
- O6. work in a team to create a prototype for solving an ethical issue caused by using data science algorithms.
Assessment
- The assessment for this course consists of various components designed to evaluate students' understanding and application of the course material. These components include individual Remindo exams (30%) and Group Projects (70%), which emphasize collaboration and teamwork. As part of these group projects, students will be required to submit state-of-the-art reports, create prototypes, and effectively present their work during both the early stages and final evaluations.
Retakes will be granted based on the overall course grade being at least a 4, rather than the grading of each individual component.
|
 |
|
Responsible Data Science is examined through the lens of 4 introductory dimensions:
- Data Dimension
- Algorithm Dimension
- Human Dimension
(a) Psychology of Human Biases
(b) Ethics / Moral Philosophy
- Design Dimension
(a) Data Visualization and Interaction Design
(b) Explainable Artificial Intelligence (XAI)
In this course, students follow lectures and workshops, read literature, engage in class discussions, give presentations, critique, and conduct an investigation on a topic related to a (self-chosen) real-world ethical problem related to data science in a particular domain. The project also contains a practical solution to the problem illustrated in a low-fidelity prototype.
Format
4 hours/week in total of either lectures or workshops (distributed in 2 days).
The format (i.e. online, hybrid or in-campus) will be determined close to the registration period and is subject to change. In all cases, lectures are not recorded (due to the nature of the course), thus time-sensitive attendance is necessary. Generally, physical attendance is expected in all classes. However, please note that non-attendance on the specific class days where grading components are being assessed will result in a zero grade for that particular component.
|
|
|