Data Science Ethics

TitleData Science Ethics
Publication TypeSyllabi
AuthorsCelis, E
PublisherYale University
Year of Publication2019
Date Published01/2019
Publication LanguageEnglish
AbstractIn this course, we will introduce, discuss, and analyze ethical issues, algorithmic challenges, and policy decisions that arise when addressing real-world problems via the lens of data science. We will do this by first grappling with the normative questions of what constitutes bias, fairness, discrimination or ethics when it comes to data science and machine learning in applications such as policing, health, journalism, and employment. We will incorporate technical precision by introducing quantitative measures that can allow us to study how algorithms codify, exacerbate and/or introduce biases of their own, and study analytic methods of correcting for or eliminating these biases. Lastly, we will study the social implications of these decisions, and understand the legal, political and policy decisions that could be used to govern data-driven decision making by making them transparent and auditable. We will read critical commentary by practitioners, state-of-the-art technical papers by data scientist and computer scientists, and samples of legal scholarship, moral and ethical philosophy, readings in sociology, and policy documents. We will often ground our discussions around recent case studies, controversies, and current events.