STAT 390

Ethical Frameworks and Moral Reasoning

Students will be able to explain and apply foundational moral theories-including utilitarianism, deontology, virtue ethics, intuitionism, and the moral teachings of Jesus Christ-to ethical challenges in data science.

Data Privacy and Ethical Data Collection

Students will recognize the importance of protecting personal data and understand ethical and legal principles related to responsible data collection, consent, ownership, and cultural context.

Statistical Integrity and Transparency

Students will identify and address threats to statistical integrity-including opaque methods, unreproducible analyses, biased reporting, and misuses of statistical evidence-and apply principles of transparency and accountability across the lifecycle of statistical, machine learning, and AI models.

Modeling and Algorithmic Impact

Students will evaluate the societal effects of statistical, machine learning, and AI models, including how bias, unfairness, or harm can emerge. They will propose ways to mitigate adverse impacts through critical reasoning, technical adjustments, and clear communication of limitations.

Moral Courage and Professional Identity

Students will explore factors that lead to unethical behavior and develop strategies for acting with integrity-especially when facing pressure, incentives, or rationalizations that encourage cutting ethical corners. They will reflect on how personal, professional, and spiritual values can guide ethical decision-making.