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Learning outcomes:
On successful completion of this course, students:
- Are familiar with the state-of-the-art research on the ethics of data science
- Are able to define and describe and recall basic concepts, principles and theories underlying data science
- Know broad classes of algorithms and how they are used for prediction, social sorting, curating, recommending, gatekeeping, experimentation, and profiling
- Know and understand the ethical implications of (algorithms in) data science on privacy, surveillance, discrimination, access to information, security, free will, human rights, social norms, etc.
- Are able to write an essay in the field of responsible data science.
- Are able to identify stakeholders and ethical implications of data science in healthcare, design, crime, education, science, job markets, business, journalism, warfare, etc.
- Are able to work in a team to create a prototype for solving an ethical issue caused by the use of data science.
Assessment:
- Individual midterm exam (30%)
- Individual paper (30%)
- Literature presentation (10%)
- Team project (30%)
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In this course, students follow lectures, read literature, engage in class discussions, and write an individual essay on a topic related to a (self-chosen) ethical problem related to data science in a particular domain.
Furthermore, each student participates in a team to find a practical solution for an ethical issue caused by the use of data science in the healthcare domain.
This solution is developed into a prototype.
Format:
- Week 1- 5: two 2-hour lectures and one 2-hour working group per week
- Week 5 or 6: Midterm exam
- Weeks 6-8: project with two 2-hour tutorials/workgroups per week
- Week 9: Presentation sessions
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