ETHICS OF DATA FOR ENGINEERS

BMI 738

Designed to educate data scientists, particularly those who work with biomedical data, about ethical and regulatory issues that may arise in the course of their research and professional interactions.

PHILOS 244

Introduction to contemporary moral and political issues in Artificial Intelligence (AI) and Data Ethics, integrating urgent problems, controversies, and continuously updated case studies. Introduces basic technical concepts such as the bias/variance tradeoff, the reference class problem, and inductive risk. Covers topics such as data and privacy, the impacts of automation on society, and the use of algorithms in medicine and criminal law.

LIS 461

An introduction to ethical, legal and policy issues related to analytics, "big data" and algorithms to support decision making. Gain familiarity with major debates and controversies in a variety of contexts. Critically analyze course materials and apply moral reasoning and legal concepts to assess case studies and critique arguments made by others.

ECE 204

A hands-on introduction to Data Science using the Python programming language. Data-centric and computational thinking. Describe, analyze, and make predictions using data from real-world phenomena. Programming in Python. Importing, manipulating, summarizing, and visualizing data of various types. Notions of bias, fairness, and ethics in data science.

ISYE 562

An examination of the "human side" of data science. Issues of bias, fairness, trust, and understandability. Unique characteristics of behavioral data, such as representative sampling, human adaptation, and grouped data. Practical skills in behavioral data analytics with a focus on important conceptual, design, and ethical issues specific to behavioral data. Survey of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, deep learning, and text analysis. Methods are contextualized through engineering case studies.