Data Ethics and Policy

3 Credits · Level: Graduate, Undergraduate

This graduate-level course will train students to navigate the landscape of ethical issues which inevitably arise, across a variety of fields and industries, in each step of the data science process. Students will explore and critically evaluate a range of data-related issues in contemporary society, such as responsible data collection, algorithmic bias, privacy, transparency, accountability, democratic participation in data usage and data-driven decisions, and the ethical implications of emerging technologies like artificial intelligence and machine learning (self-driving cars, ChatGPT, crowd-sourced training data, etc.). Through a combination of theoretical discussions and real-world case studies, students will examine the profound public policy and social issues associated with data ethics. This course will empower students by introducing a set of general ethical frameworks (consequentialism, deontological ethics, and virtue ethics) and discussing their relative strengths and weaknesses in terms of their ability to address modern ethical dilemmas and guide ethical decision-making processes in business, healthcare, government, and academia. These theoretical frameworks will then be discussed in light of more practical regulatory and policy considerations, so that students will have the tools they need to draw conclusions (in the form of their final projects) about best practices for data handling within a particular field or topic of interest to them. The course will thus equip students with a robust ethical "toolbox" for conscientiously gathering, interpreting, and extracting meaning from data while respecting privacy, fairness, transparency, democratic accountability, and other social concerns.