Students are able to:
- recognize and describe societal and ethical issues surrounding data science;
- describe the theories of data determinism and value-sensitive design, identify conflicts between these theories, and take a reasoned position on how to resolve these conflicts;
- recognize and describe the main lines of major contemporary theories of ethics, and be able to apply these theories to evaluate practices in data science and the uses of "big data"
- recognize, analyze, and describe major value concepts relevant to data science and how they can be implemented in technology and institutional design;
- recognize, analyze, and describe traditional values in science and the ethics of science, and how these traditional values encounter conflicts in the context of data science;
- develop well-reasoned positions in the societal and ethical debates surrounding data science in different domains of application;
- use research tools to investigate societal and ethical debates surrounding data science, and be motivated to do so.
Data scientists extract information from vast datasets about people, technological systems, companies, and the natural world for commercial, scientific and practical purposes. In this course, we introduce students to aspects of ethical thinking related to big data and familiarize them with applying ethical thinking to specific domains of data science application. Among these aspects of ethical thinking are basic ethical theories; technological and data determinism; value-sensitive design; specific values of consent, freedom, and identity; scientific values such as verifiability and openness; and techno-regulation of big data.|
In this course we investigate what ethical frameworks should guide the real-world activities of data scientists, as they aspire to produce accurate, profitable, and useful applications of new approaches to 'big data'; We examine these issues using case studies from current practice that raise value issues. Case studies considered in the course may include the use of private data from individuals and groups by both startups and established companies; the difficulty of sharing large data sets and the algorithms used to make sense of them, and its implications for scientific practice; and real-life difficulties of regulating data use in line with societal values.
Type of instructions
- Interactive lectures
- Group work on case studies, including discussion, presentation and peer feedback
Type of exams
- Reading Quizzes (10%)
- Group Assignment: Presentation (20%)
- Group Assignment: Written report (20%)
- Individual Assignment: Written report (50%)