Data Science Ethics: Addressing Big Data Challenges

Module 1 establishes a basic foundation in the notion of simple utilitarian ethics used for this course. The lecture material and quiz questions are designed to get most people to come to an agreement about right and wrong, using the utilitarian framework taught here.

This module discusses the laws that govern the Principle of Informed Consent, and why informed consent doesn't work well for retrospective studies or for customers of electronic businesses.

This module explores the question of who owns data about you, including examples of personal data ownership and limits on recording and use of data.

This module examines privacy as a basic human need, the relationship between services provided and data exchanged, and compares data against metadata.

This module explores anonymous transactions, including block chains and bitcoin, and looks at some of the drawbacks that come with anonymity.

This module focuses on common errors in data science methods leading to erroneous conclusions, with emphasis on the notion of representative sample.

This module discusses how algorithmic decisions can be unfair, despite the apparent neutrality of data-driven analysis.

This module considers societal consequences of Data Science, including ossification, information asymmetry, surveillance, and predictive policing.

This module ties all the issues considered together into a simple, two-point code of ethics for the practitioner.

This module contains lists of attributions for the external audio-visual resources used throughout the course.