Ethics Education in Data Science: Classroom Topics and Assignments

The creation of ethics modules that can be inserted into a variety of classes may help ensure that ethics as a subject is not marginalized and enable professors with little experience in philosophy or with fewer resources to incorporate ethics into their more technical classes. This post will outline some of the topics that professors have decided to cover in this field, as well as suggestions for types of assignments that may be useful. We hope that readers will consider ways to add these into their classes, and we welcome comments with further suggestions of topics or assignments. With regards to ethics, some of the key topics that professors have taught about include: deontology, consequentialism, utilitarianism, virtue ethics, moral responsibility, cultural relativism, social contract, feminist ethics, justice consequentialism, the distinction between ethics and law, and the relationship between principles, standards, and rules. Using these frameworks, professors can discuss a variety of topics, including: privacy, algorithmic bias, misinformation, intellectual property, surveillance, inequality, data collection, AI governance, free speech, transparency, security, anonymity, systemic risk, labor, net neutrality, accessibility, value-sensitive design, codes of ethics, predictive policing, virtual reality, ethics in industry, machine learning, clinical versus actuarial reasoning, issue spotting, and basic social science concepts.