DS 435Ethical Issues in Data Science Practice
This course explores social and ethical dimensions of data science. Datafication can be a powerful force for good, but it can also do harm-to individuals and society. Oriented primarily around case studies, the course investigates when, why, and how data is collected, analyzed, and used, and explores the ethical stakes of data-driven systems. In addition to diagnosing ethical problems-e.g., invasions of privacy, algorithmic bias, and lack of transparency and accountability-students are asked to think creatively and constructively about how the tools of data science can be used to realize shared ethical and social commitments. The course will be comprised of both "theory" and "lab" components. The former will contextualize ethical problems, introducing students to ethical theories and frameworks for addressing them. The latter will ask students to put those ideas to work, using the tools of data science to identify examples of ethical issues in data science practice, and proposing means of addressing them.
Prerequisites
You need:
Where it sits in degree plans
Suggested plans most often put it in Year 4 · Fall (3 programs).
Required in (5)
- Data Sciences, B.S. (Abington)
- Data Sciences, B.S. (Engineering)
- Data Sciences, B.S. (Information Sciences and Technology)
- Data Sciences, B.S. (Science)
- Information Technology Ethics and Compliance, B.S.
One of the options in (1)
- Functional Data Analytics, B.S.