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.
For a retake the grade of the original test has to be at least a 4.
- Remindo Exams 25 % (with proctoring, if not on-campus)
- Presentation of an Academic Paper 10%
The paper is self-chosen from a given list.
- Group Project
(a) Group Presentation: Journalist Overview 2%
(b) Group Presentation % Preliminary Prototype: Final Project 8%
(c) Group Essay & Prototype 30 %
Topic is self-chosen, though a recommended list is provided.
- Personal Essay 25% (+ up to 10% bonus, for exceptional essays only).
The topic is a critique, both conceptual and technical, of a given group essay.
Responsible Data Science is examined through the lens of 4 introductory dimensions:
In this course, students follow lectures and workshops, read literature, engage in class discussions, give presentations, critique, and write an original essay 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.
- 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)
6 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.