Upon successful completion of this course, the students will be able to:
- Name and explain key privacy, data protection law and ethics concepts and principles relevant for data science [know and understand];
- Apply these key concepts and principles to real-life and hypothetical instances of data science, e.g. identify the challenges that the data science scenarios pose to the legal and ethical principles and propose how the ethical and legal concerns about data science can be addressed in a real-life or a hypothetical data science scenario [apply];
- Form reasoned opinions about data science based on these key legal and ethical concepts and principles [assess].
The reading list for the course will be provided on the Canvas at least 2 weeks before start of the course.
To facilitate meaningful discussions in class, students are expected to read the assigned literature for every lecture to be ready to answer questions about it.
Written exam: It is a closed book exam, meaning that the students are not allowed to bring any materials to the exam room. A resit of the written exam is allowed.
The written assignment is an essay where students are asked, based on the key legal and ethical concepts and principles studied in the course, to form a reasoned opinion about a real-life or hypothetical instance of data science. Please check Canvas for precise formulation of the actual assignment.
Data science is profoundly and tangibly affecting business and society. Artificial Intelligence and Machine Learning have already been used in the sector of finance, healthcare, policymaking, public order and law enforcement. Mining growing pools of data using advanced algorithms promises to generate new insights into human behaviour and provide solutions to a variety of social problems like effective resource management, enforcement of public order, traffic control, accurate and fast diagnostics, and treatment and prevention of disease. At the same time, accumulation of unprecedented amount of data and the new horizons it opens present risks for individuals, groups, and societies, revealing sensitive knowledge about people based on seemingly innocuous data, giving power over groups and societies. Data science algorithms often contain bias and reinforce social inequality, and their outcomes do not lend themselves easily for explanation and scrutiny.
For this reason, it is important that the data science applications are designed and used, and value is extracted from data in a way that is secure and sustainable, meaning that the risks are eliminated or mitigated, individuals are protected, and fundamental values of the society endure in the face of the innovative data processing techniques.
Type of instructions
Interactive lectures and self-study.
Type of exams
1. A paper assignment - 30% of the final grade; a good-faith submission of the assignment is required to be admitted to the final written exam.
2. Written exam - 70% of the final grade. The resit for the written exam functions as a resit for the entire course, i.e. all evaluation elements.