This course explores the many ethical dilemmas that arise in the contemporary practice of data science and creates a framework for comprehending these concerns. The impact of unethical behavior and how data are used ethically in society will be covered. The Findable, Accessible, Interoperable, and Reusable (FAIR) data principles are described. By the end of the course, students will be able to recognize and carefully differentiate between problems unique to data science and those that arise as a result of using other related computational methods of analysis, such as machine learning and artificial intelligence. They will also be able to distinguish between ethical misconduct unique to data science methods and those that may result from the contexts in which these methods are used.
- Enseignant: Sara Salehi