STOR 390 delves into the intersection of machine learning, ethics, and society, exploring the ethical dilemmas arising from modern statistical methods. The course focuses on technical solutions to these dilemmas, leveraging philosophical principles to address ambiguous cases, and effective communication of integrated results and positions.

• Explore ethical dilemmas in statistical methods
• Consider technical solutions to ethical dilemmas
• Leverage philosophical principles in ambiguous situations
• Communicate and present results effectively

• Statistical: Classification, Sensitivity Analysis, Federated Learning, Causal Inference
• Coding: R-Markdown
• Philosophical: Deontology, Consequentialism, Virtue Ethics, Harm Principle, Informed Consent

• STOR 120 or comparable course
• Additional courses like STOR 320 and STOR 455 may be beneficial