Data science has the potential to be both beneficial and detrimental to individuals and/or the wider public. To help minimize any adverse effects, we must seek to understand the potential impact of our work and consider any opportunities that may deliver benefits for the public. It is recognized that not all work will have a defined societal benefit, but we could strive to seek fairness or an overall increase to well being, within commercial applications.

In this course, we will explore the moral, social, and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in the analysis. Through class discussions, case studies, and exercises, students will learn the basics of ethical thinking, understand some tools to check or mitigate undesired effects, and study the distinct challenges associated with ethics in modern data science.