Reading Lists

The listed readings are primarily pulled from the collated data science ethics courses and are in no way exhaustive. For additional readings, see the references of the readings we list under each ethics topic.

Alignment

Readings on the moral alignment between human values and current data science practices.

Bias, Fairness, and Justice

Readings on the normative concepts of bias, fairness, and justice and how they are relevant considerations in data science.

Causation

Readings on causation as it applies to data science, and more specifically data science ethics. A primary focus of these readings is causal inference and socially sensitive attributes (e.g., race and gender).

Characterizations of Data and Data Science

Readings on how to characterize data, data science, artificial intelligence, and data science ethics.

Consent

Readings on informed consent in data science practices, why it ethically matters, and how to grapple with the practical challenges of obtaining informed consent in data science.

Democracy

Readings and case studies on the applications of data science in democracy and the ethical implications of using statistical models in such contexts.

Explainability, Intepretability, and Transparency

Readings on what is meant by “explainability” (and related terms like “transparency” and “interpretability”) in data science and to what extent acheiving explainabilty (or transparency or interpretability) in algorithms is morally important.

Predictive Policing

Readings and case studies on the applications of data science in predictive policing and the ethical implications of using statistical models in such settings.

Privacy

Readings on privacy, why it matters, its relationship to consent, and case studies that emphasize both the importance and difficulty of ensuring privacy in data science.

Responsibility

Readings concerning moral responsibility in data science as well as some of the challenges in assessing who is morally responsible for data models and predictions.

Workplace

Readings and case studies on the applications of data science in the workplace (e.g., in hiring or promotion) and the ethical implications of using statistical models in such contexts.